GeneticAlgorithmReflector.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "89f983f5",
   "metadata": {},
   "source": [
    "# Genetic algorithm optimization of a silicon on-chip reflector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c129793b",
   "metadata": {},
   "source": [
    "**Note: the cost of running the entire notebook is larger than 10 FlexCredits.**\n",
    "\n",
    "A [genetic algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) (GA) is a search heuristic that mimics the process of natural selection. This algorithm reflects the process of natural evolution where the fittest individuals are selected for reproduction in order to produce offspring of the next generation.\n",
    "\n",
    "The steps in a GA are typically as follows:\n",
    "\n",
    "1. **Initial Population**: The process begins with a set of individuals which is called a population. Each individual is a solution to the problem you want to solve.\n",
    "\n",
    "2. **Fitness Function**: Each individual in the population has a fitness score which indicates how good it is at solving the problem.\n",
    "\n",
    "3. **Selection**: The algorithm selects individuals, often the fittest among the population, to breed a new generation. The selection can be done in various ways, such as roulette wheel selection, tournament selection, etc.\n",
    "\n",
    "4. **Crossover**: During crossover, parts of two individuals' chromosome strings are swapped to get a new offspring, which may contain some parts of both parents' strings. This simulates reproduction and biological crossover.\n",
    "\n",
    "5. **Mutation**: In some new offspring, random genes are mutated or changed to maintain diversity within the population and to avoid premature convergence.\n",
    "\n",
    "6. **New Generation**: The new generation of population is formed by the offspring. This new generation is then used in the next iteration of the algorithm.\n",
    "\n",
    "7. **Termination**: The algorithm terminates if the population has converged (does not produce offspring that are significantly different from the previous generation), or a satisfactory solution has been found, or a set number of generations have been produced.\n",
    "\n",
    "GAs have become a powerful tool for optimizing photonic components, leveraging their ability to efficiently search large and complex design spaces. In photonics, where the performance of components like waveguides, photonic crystals, and fibers can be highly sensitive to geometrical and material parameters, GAs offer a way to find optimal solutions that might be difficult to discover using traditional design methods. \n",
    "\n",
    "In this notebook, we demonstrate the optimization of a compact silicon waveguide reflector using GA, utilizing the open source Python library [pyGAD](https://pygad.readthedocs.io/en/latest/) implemented into the `Tidy3D` Design plugin. The idea follows `Zejie Yu, Haoran Cui, and Xiankai Sun, \"Genetically optimized on-chip wideband ultracompact reflectors and Fabry–Perot cavities,\" Photon. Res. 5, B15-B19 (2017)` [DOI:10.1364/PRJ.5.000B15](https://doi.org/10.1364/PRJ.5.000B15). The design region of the reflector consists of a grid divided into 18x18 pixels. Each pixel is a square measuring 120 nm by 120 nm. \n",
    "\n",
    "<img src=\"img/optimized_reflector.png\" width=\"450\" alt=\"Schematic of the reflector\">\n",
    "\n",
    "`Tidy3D` is a powerful tool for photonic design optimization due to its fast speed and high throughput. Besides GA, we have demonstrated particle swarm optimizations of a [polarization beam splitter](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/) and a [bullseye cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/), CMA-ES optimization of an [waveguide S-bend](https://www.flexcompute.com/tidy3d/examples/notebooks/SbendCMAES/), Bayesian optimization of a [Y-junction](https://www.flexcompute.com/tidy3d/examples/notebooks/BayesianOptimizationYJunction/), and direct binary search optimization of an [optical switch](https://www.flexcompute.com/tidy3d/examples/notebooks/OpticalSwitchDBS/). Furthermore, we also have a growing list of gradient-based adjoint optimization examples including\n",
    "\n",
    "1. [Mode converter](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin3InverseDesign/),\n",
    "\n",
    "2. [Waveguide taper](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin5BoundaryGradients/),\n",
    "\n",
    "3. [Metalens](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin7Metalens/),\n",
    "\n",
    "4. [Waveguide bend](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin8WaveguideBend/), \n",
    "\n",
    "5. [Multiplexer](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin9WDM/),\n",
    "\n",
    "6. [Y-branch](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin10YBranchLevelSet/),\n",
    "\n",
    "7. [MZI circuit](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin11CircuitMZI/),\n",
    "\n",
    "8. [Light extractor](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin12LightExtractor/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9979b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment the following line to install pygad if it's not installed in your environment already\n",
    "# pip install pygad\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "import tidy3d.plugins.design as tdd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb19411a",
   "metadata": {},
   "source": [
    "## Simulation Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fd3302",
   "metadata": {},
   "source": [
    "For simplicity, we will use the silicon and oxide media directly from the [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html#)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e01ad5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Si = td.material_library[\"cSi\"][\"Palik_Lossless\"]\n",
    "SiO2 = td.material_library[\"SiO2\"][\"Palik_Lossless\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7a4663",
   "metadata": {},
   "source": [
    "The simulation wavelength range is 1450 nm to 1650 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "531341c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda0 = 1.55  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "ldas = np.linspace(1.45, 1.65, 101)  # wavelength range\n",
    "freqs = td.C_0 / ldas  # frequency range\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the source frequency range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c58826ce",
   "metadata": {},
   "source": [
    "The waveguide has a 500 nm width and 220 nm thickness. The design region consists of 18 by 18 pixels. Each pixel is 120 nm by 120 nm. Due to the symmetry, we only consider symmetric design so the total number of tunable pixels is 18*9=162."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70707ef4",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = 0.5  # width of the waveguide\n",
    "t = 0.22  # thickness of the silicon\n",
    "l = 1  # length of the waveguide in the simulation\n",
    "Px = Py = 0.12  # pixel sizes in the x and y directions\n",
    "Nx = 18  # number of pixels in the x direction\n",
    "Ny = 9  # numbre of pixels in the y direction\n",
    "buffer = 0.8  # buffer spacing\n",
    "res = 15 # overall resolution setting (steps per wavelength)\n",
    "gsx = gsy = 5 # number of grid steps per pixel in override region"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6e1510",
   "metadata": {},
   "source": [
    "We will create an array of length 162 to represent a design. Each element corresponds to each pixel in the design region. An element value of 1 means the pixel is silicon while an element value of 0 means the pixel is void. To facilitate the optimization, we define a helper function `create_design(pixels)` that takes the pixel array and creates the [Structures](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html) of the simulation, inlcuding the waveguide and the design region. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04b74f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_design(pixels):\n",
    "    geo = 0\n",
    "    for i, pixel in enumerate(pixels):\n",
    "        if pixel == 1:\n",
    "            geo += td.Box(\n",
    "                center=(l + Px / 2 + Px * (i % Nx), Py * Ny - Py / 2 - Py * (i // Nx), t / 2),\n",
    "                size=(Px, Py, t),\n",
    "            )\n",
    "            geo += td.Box(\n",
    "                center=(l + Px / 2 + Px * (i % Nx), -(Py * Ny - Py / 2 - Py * (i // Nx)), t / 2),\n",
    "                size=(Px, Py, t),\n",
    "            )\n",
    "    geo = geo + td.Box(center=(0, 0, t / 2), size=(2 * l, w, t))\n",
    "\n",
    "    design = td.Structure(geometry=geo, medium=Si)\n",
    "\n",
    "    return design"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a87700",
   "metadata": {},
   "source": [
    "As a quick check, we create a random array and plot the created structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3fc9d4e5",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pixels = np.random.choice([0, 1], size=Nx * Ny)\n",
    "design = create_design(pixels)\n",
    "design.plot(z=t / 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93cfd3ca",
   "metadata": {},
   "source": [
    "Furthermore, we define a `make_sim(pixels)` function to define the entire simulation given the pixel array. The simulation includes a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) to launch the TE0 mode at the waveguide and a [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) next to the source to measure the reflection. To minimize data download during the optimization, we will only record the reflection at the central wavelength of 1550 nm since the objective is to maximize this value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd22e367",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_sim(pixels):\n",
    "\n",
    "    design = create_design(pixels)\n",
    "\n",
    "    # Add a mode source as excitation\n",
    "    mode_spec = td.ModeSpec(num_modes=1, target_neff=3.5)\n",
    "    mode_source = td.ModeSource(\n",
    "        center=(l / 2, 0, t / 2),\n",
    "        size=(0, 4 * w, 6 * t),\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "        direction=\"+\",\n",
    "        mode_spec=mode_spec,\n",
    "        mode_index=0,\n",
    "    )\n",
    "\n",
    "    # Add a mode monitor to measure transmission at the output waveguide\n",
    "    mode_monitor = td.ModeMonitor(\n",
    "        center=(l / 4, 0, t / 2),\n",
    "        size=mode_source.size,\n",
    "        freqs=[freq0],\n",
    "        mode_spec=mode_spec,\n",
    "        name=\"mode\",\n",
    "    )\n",
    "\n",
    "    # Define simulation domain size\n",
    "    Lx = l + Nx * Px + buffer\n",
    "    Ly = 2 * Ny * Py + 2 * buffer\n",
    "    Lz = 10 * t\n",
    "\n",
    "    eff_inf = 1e2  # effective infinity\n",
    "\n",
    "    # Define substrate structure\n",
    "    substrate = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-eff_inf, -eff_inf, -eff_inf), rmax=(eff_inf, eff_inf, 0)\n",
    "        ),\n",
    "        medium=SiO2,\n",
    "    )\n",
    "\n",
    "    run_time = 5e-13  # simulation run time\n",
    "\n",
    "    # Simulation box\n",
    "    sim_box = td.Box.from_bounds(rmin=(0, -Ly / 2, -Lz / 2), rmax=(Lx, Ly / 2, Lz / 2))\n",
    "\n",
    "    # Mesh override structure over the pixel region to ensure grid that conforms to the pixels\n",
    "    mesh_override = td.MeshOverrideStructure(\n",
    "        geometry=td.Box.from_bounds(rmin=(l, -Py * Ny, 0), rmax=(l + Px * Nx, Py * Ny, 0)),\n",
    "        dl=(Px / gsx, Py / gsy, 1), # the z-direction dl doesn't matter as the box size is 0 along z\n",
    "    )\n",
    "\n",
    "    # Define simulation\n",
    "    sim = td.Simulation(\n",
    "        center=sim_box.center,\n",
    "        size=sim_box.size,\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=res, wavelength=lda0, override_structures=[mesh_override]),\n",
    "        structures=[design, substrate],\n",
    "        sources=[mode_source],\n",
    "        monitors=[mode_monitor],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "        symmetry=(0, -1, 0),\n",
    "    )\n",
    "\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174fe54c",
   "metadata": {},
   "source": [
    "Again, we test it by defining a simulation with a random pixel array. From the 3D view, we confirm that the simulation setup is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc3e6ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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{&quot;attrs&quot;: {}, &quot;name&quot;: null, &quot;type&quot;: &quot;PML&quot;, &quot;num_layers&quot;: 12, &quot;parameters&quot;: {&quot;attrs&quot;: {}, &quot;sigma_order&quot;: 3, &quot;sigma_min&quot;: 0.0, &quot;sigma_max&quot;: 1.5, &quot;type&quot;: &quot;PMLParams&quot;, &quot;kappa_order&quot;: 3, &quot;kappa_min&quot;: 1.0, &quot;kappa_max&quot;: 3.0, &quot;alpha_order&quot;: 1, &quot;alpha_min&quot;: 0.0, &quot;alpha_max&quot;: 0.0}}, &quot;type&quot;: &quot;Boundary&quot;}, &quot;type&quot;: &quot;BoundarySpec&quot;}, &quot;monitors&quot;: [{&quot;attrs&quot;: {}, &quot;type&quot;: &quot;ModeMonitor&quot;, &quot;center&quot;: [0.25, 0.0, 0.11], &quot;size&quot;: [0.0, 2.0, 1.32], &quot;name&quot;: &quot;mode&quot;, &quot;interval_space&quot;: [1, 1, 1], &quot;colocate&quot;: false, &quot;freqs&quot;: [193414489032258.06], &quot;apodization&quot;: {&quot;attrs&quot;: {}, &quot;start&quot;: null, &quot;end&quot;: null, &quot;width&quot;: null, &quot;type&quot;: &quot;ApodizationSpec&quot;}, &quot;mode_spec&quot;: {&quot;attrs&quot;: {}, &quot;num_modes&quot;: 1, &quot;target_neff&quot;: 3.5, &quot;num_pml&quot;: [0, 0], &quot;filter_pol&quot;: null, &quot;angle_theta&quot;: 0.0, &quot;angle_phi&quot;: 0.0, &quot;precision&quot;: &quot;single&quot;, &quot;bend_radius&quot;: null, &quot;bend_axis&quot;: null, &quot;track_freq&quot;: &quot;central&quot;, &quot;group_index_step&quot;: false, &quot;type&quot;: &quot;ModeSpec&quot;}, &quot;store_fields_direction&quot;: null}], &quot;grid_spec&quot;: {&quot;attrs&quot;: {}, &quot;grid_x&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;AutoGrid&quot;, &quot;min_steps_per_wvl&quot;: 15.0, &quot;max_scale&quot;: 1.4, &quot;dl_min&quot;: 0.0, &quot;mesher&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;GradedMesher&quot;}}, &quot;grid_y&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;AutoGrid&quot;, &quot;min_steps_per_wvl&quot;: 15.0, &quot;max_scale&quot;: 1.4, &quot;dl_min&quot;: 0.0, &quot;mesher&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;GradedMesher&quot;}}, &quot;grid_z&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;AutoGrid&quot;, &quot;min_steps_per_wvl&quot;: 15.0, &quot;max_scale&quot;: 1.4, &quot;dl_min&quot;: 0.0, &quot;mesher&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;GradedMesher&quot;}}, &quot;wavelength&quot;: 1.55, &quot;override_structures&quot;: [{&quot;attrs&quot;: {}, &quot;geometry&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;Box&quot;, &quot;center&quot;: [2.08, 0.0, 0.0], &quot;size&quot;: [2.16, 2.16, 0.0]}, &quot;name&quot;: null, &quot;autograd_background_permittivity&quot;: null, &quot;type&quot;: &quot;MeshOverrideStructure&quot;, &quot;dl&quot;: [0.024, 0.024, 1.0], &quot;enforce&quot;: false}], &quot;snapping_points&quot;: [], &quot;type&quot;: &quot;GridSpec&quot;}, &quot;version&quot;: &quot;2.8.0rc1&quot;, &quot;plot_length_units&quot;: &quot;\\u03bcm&quot;, &quot;lumped_elements&quot;: [], &quot;subpixel&quot;: {&quot;attrs&quot;: {}, &quot;dielectric&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;PolarizedAveraging&quot;}, &quot;metal&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;Staircasing&quot;}, &quot;pec&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;PECConformal&quot;, &quot;timestep_reduction&quot;: 0.3}, &quot;type&quot;: &quot;SubpixelSpec&quot;}, &quot;courant&quot;: 0.99, &quot;normalize_index&quot;: 0, &quot;shutoff&quot;: 1e-05, &quot;run_time&quot;: 5e-13}\" />\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://feature-simulation-viewer.d3a9gfg7glllfq.amplifyapp.com/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {{\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){{\n",
       "                                var simulation = JSON.parse(node.dataset.simulation);\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: simulation}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }}\n",
       "                        }};\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = make_sim(pixels)\n",
    "sim.plot_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a25227f1",
   "metadata": {},
   "source": [
    "## GA Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e62a2da5",
   "metadata": {},
   "source": [
    "Now we are ready to setup a GA optimization. This can quickly be done using the Tidy3D `Design` plugin, which can efficiently manage the optimization with parallel cloud computing, reducing the runtime and saving FlexCredits.\n",
    "\n",
    "First we need to define some hyperparameters. In this particular example, we will use 30 solutions per population and run the optimization for a total of 25 generations, and also specify early-stop criteria that stops the optimization if the fitness value doesn't change for 6 consecutive generations. The `MethodGenAlg` class can be used to tune a number of parameters that change the selection, crossover, and mutation processes. We only change a few and leave the most to the default settings. Users are encouraged to explore these settings using the [PyGAD](https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class) docs and fine tune them for better results. For reproducibility, we also set a fixed random seed.\n",
    "\n",
    "A list `ParameterInt` objects are created to store the binary values used to define the geometry of the reflector. These parameters and the GA method are supplied to a `DesignSpace` object, which manages the GA process and outputs the results of the optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a60323d-aac4-44a0-a855-e3f3e7057027",
   "metadata": {},
   "outputs": [],
   "source": [
    "method = tdd.MethodGenAlg(\n",
    "    solutions_per_pop=30, \n",
    "    n_generations=25,\n",
    "    n_parents_mating=10,\n",
    "    keep_elitism=1,\n",
    "    parent_selection_type=\"sss\",\n",
    "    crossover_type=\"single_point\",\n",
    "    crossover_prob=0.7,\n",
    "    mutation_type=\"inversion\",\n",
    "    mutation_prob=None,\n",
    "    seed=5,\n",
    "    stop_criteria_type=\"saturate\",\n",
    "    stop_criteria_number=6,\n",
    ")\n",
    "\n",
    "parameters = [tdd.ParameterInt(name=i, span=(0, 1)) for i in range(Nx * Ny)]\n",
    "\n",
    "design_space = tdd.DesignSpace(method=method, parameters=parameters, task_name=\"GA_Notebook\", path_dir=\"./data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68b26822",
   "metadata": {},
   "source": [
    "We can optionally summarise the design space to check the setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92509ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary = design_space.summarize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be63767",
   "metadata": {},
   "source": [
    "To run the GA optimization we need to define the `fn_pre` and `fn_post` functions for the optmizer. The pre function creates a `Simulation` from the parameters suggested by GA as a solution to the reflector. The post function defines how the `SimulationData` should be evaluated to create a single float value which is then fed back into the GA. By evaluating of the float values of the current generation the GA can then assemble the population for the next generation, based on our hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4873608a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn_pre(**params):\n",
    "    pixels = np.array(list(params.values()))\n",
    "    sim = make_sim(pixels)\n",
    "    \n",
    "    return sim\n",
    "\n",
    "def fn_post(sim_data):\n",
    "    return abs(sim_data[\"mode\"].amps.sel(direction=\"-\").squeeze(drop=True).values) ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94a1d492",
   "metadata": {},
   "source": [
    "Once satisfied with the setup, the optimization can be started by calling `DesignSpace.run` with our `fn_pre` and `fn_post` functions. The `DesignSpace` will parallelize each generation by running the `Simulation` objects as a batch, and will prevent previously evaluated solutions from being recomputed. Once complete, we can combine all the solution outputs within a Pandas `DataFrame` for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "191a78ce",
   "metadata": {},
   "outputs": [
    {
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       "\u001b[2;36m09:43:21 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "\u001b[2;36m09:44:43 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m1\u001b[0m Best Fitness: \u001b[1;36m0.723\u001b[0m                                   \n"
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       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m09:44:44 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "</pre>\n"
      ],
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       "\u001b[2;36m09:46:02 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m2\u001b[0m Best Fitness: \u001b[1;36m0.760\u001b[0m                                   \n"
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       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m09:46:03 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:49:36 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.760</span>                                   \n",
       "</pre>\n"
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       "\u001b[2;36m09:49:36 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m3\u001b[0m Best Fitness: \u001b[1;36m0.760\u001b[0m                                   \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:49:37 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
       "</pre>\n"
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       "\u001b[2;36m09:49:37 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:50:59 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.760</span>                                   \n",
       "</pre>\n"
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       "\u001b[2;36m09:50:59 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m4\u001b[0m Best Fitness: \u001b[1;36m0.760\u001b[0m                                   \n"
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       "</pre>\n"
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       "\u001b[2;36m09:51:00 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:52:25 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.765</span>                                   \n",
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       "\u001b[2;36m09:52:25 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m5\u001b[0m Best Fitness: \u001b[1;36m0.765\u001b[0m                                   \n"
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       "</pre>\n"
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       "\u001b[2;36m09:52:26 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:53:47 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.765</span>                                   \n",
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      ],
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       "\u001b[2;36m09:53:47 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m6\u001b[0m Best Fitness: \u001b[1;36m0.765\u001b[0m                                   \n"
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       "</pre>\n"
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:55:12 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.784</span>                                   \n",
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       "\u001b[2;36m09:55:12 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m7\u001b[0m Best Fitness: \u001b[1;36m0.784\u001b[0m                                   \n"
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:56:32 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.784</span>                                   \n",
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       "\u001b[2;36m09:56:32 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m8\u001b[0m Best Fitness: \u001b[1;36m0.784\u001b[0m                                   \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:56:33 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
       "</pre>\n"
      ],
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       "\u001b[2;36m09:56:33 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:57:51 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.803</span>                                   \n",
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       "\u001b[2;36m09:57:51 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m9\u001b[0m Best Fitness: \u001b[1;36m0.803\u001b[0m                                   \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:57:52 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
       "</pre>\n"
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       "\u001b[2;36m09:57:52 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:59:08 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.816</span>                                  \n",
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      ],
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       "\u001b[2;36m09:59:08 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m10\u001b[0m Best Fitness: \u001b[1;36m0.816\u001b[0m                                  \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">09:59:09 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
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       "\u001b[2;36m09:59:09 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:00:30 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.816</span>                                  \n",
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      ],
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       "\u001b[2;36m10:00:30 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m11\u001b[0m Best Fitness: \u001b[1;36m0.816\u001b[0m                                  \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:00:31 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
       "</pre>\n"
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       "\u001b[2;36m10:00:31 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:01:46 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.829</span>                                  \n",
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      ],
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       "\u001b[2;36m10:01:46 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m12\u001b[0m Best Fitness: \u001b[1;36m0.829\u001b[0m                                  \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:01:47 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
       "</pre>\n"
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       "\u001b[2;36m10:01:47 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:03:05 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.829</span>                                  \n",
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       "\u001b[2;36m10:03:05 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m13\u001b[0m Best Fitness: \u001b[1;36m0.829\u001b[0m                                  \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:03:06 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
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       "\u001b[2;36m10:03:06 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:04:24 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.829</span>                                  \n",
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       "\u001b[2;36m10:04:24 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m14\u001b[0m Best Fitness: \u001b[1;36m0.829\u001b[0m                                  \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:04:25 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
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       "\u001b[2;36m10:04:25 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:05:44 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.866</span>                                  \n",
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       "\u001b[2;36m10:05:44 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m15\u001b[0m Best Fitness: \u001b[1;36m0.866\u001b[0m                                  \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:05:45 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
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       "\u001b[2;36m10:05:45 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:07:03 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.866</span>                                  \n",
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       "\u001b[2;36m10:07:03 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m16\u001b[0m Best Fitness: \u001b[1;36m0.866\u001b[0m                                  \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:07:04 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:08:18 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.912</span>                                  \n",
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       "\u001b[2;36m10:08:18 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m17\u001b[0m Best Fitness: \u001b[1;36m0.912\u001b[0m                                  \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:08:19 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:09:39 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.912</span>                                  \n",
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       "\u001b[2;36m10:09:39 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m18\u001b[0m Best Fitness: \u001b[1;36m0.912\u001b[0m                                  \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:09:40 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:09:40 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:10:57 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.912</span>                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:10:57 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m19\u001b[0m Best Fitness: \u001b[1;36m0.912\u001b[0m                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:10:58 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:10:58 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:12:18 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.912</span>                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:12:18 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m20\u001b[0m Best Fitness: \u001b[1;36m0.912\u001b[0m                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:12:19 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:12:19 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:13:40 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.912</span>                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:13:40 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m21\u001b[0m Best Fitness: \u001b[1;36m0.912\u001b[0m                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:13:41 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:13:41 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:15:00 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.912</span>                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:15:00 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m22\u001b[0m Best Fitness: \u001b[1;36m0.912\u001b[0m                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:15:01 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span> Simulations                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:15:01 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m29\u001b[0m Simulations                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:16:16 BST </span>Generation <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23</span> Best Fitness: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.912</span>                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:16:16 BST\u001b[0m\u001b[2;36m \u001b[0mGeneration \u001b[1;36m23\u001b[0m Best Fitness: \u001b[1;36m0.912\u001b[0m                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Best Result: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.912478548154955</span>                                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Best Parameters: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">31</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">32</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">34</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">35</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">36</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">37</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">38</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">39</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">42</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">43</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">44</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">45</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">46</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">47</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">48</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">49</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">50</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">51</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">52</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">53</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">54</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">55</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">56</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">57</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">58</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">59</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">60</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">61</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">62</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">63</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">64</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">65</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">66</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">67</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">68</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">69</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">70</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">71</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">72</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">73</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">74</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">75</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">76</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">77</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">78</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">79</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">80</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">81</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">82</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">83</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">84</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">85</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">86</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">87</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">88</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">89</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">90</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">91</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">92</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">93</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">94</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">95</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">96</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">97</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">98</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">99</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">101</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">102</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">103</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">104</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">105</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">106</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">107</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">108</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">109</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">110</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">111</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">112</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">113</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">114</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">115</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">116</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">117</span>:\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">118</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">119</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">120</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">121</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">122</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">123</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">124</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">125</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">126</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">127</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">128</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">129</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">130</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">131</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">132</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">133</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">134</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">135</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">136</span>:\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">137</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">138</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">139</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">140</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">141</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">142</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">143</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">144</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">145</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">146</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">147</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">148</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">149</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">150</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">151</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">152</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">153</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">154</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">155</span>:\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">156</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">157</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">158</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">159</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">160</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">161</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>                        \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBest Result: \u001b[1;36m0.912478548154955\u001b[0m                                     \n",
       "\u001b[2;36m             \u001b[0mBest Parameters: \u001b[1;36m0\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m1\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m2\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m3\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m4\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m5\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m6\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m7\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m8\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m9\u001b[0m: \u001b[1;36m0\u001b[0m \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m10\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m11\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m12\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m13\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m14\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m15\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m16\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m17\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m18\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m19\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m20\u001b[0m: \u001b[1;36m0\u001b[0m  \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m21\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m22\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m23\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m24\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m25\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m26\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m27\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m28\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m29\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m30\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m31\u001b[0m: \u001b[1;36m1\u001b[0m  \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m32\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m33\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m34\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m35\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m36\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m37\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m38\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m39\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m40\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m41\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m42\u001b[0m: \u001b[1;36m0\u001b[0m  \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m43\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m44\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m45\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m46\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m47\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m48\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m49\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m50\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m51\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m52\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m53\u001b[0m: \u001b[1;36m1\u001b[0m  \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m54\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m55\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m56\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m57\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m58\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m59\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m60\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m61\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m62\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m63\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m64\u001b[0m: \u001b[1;36m0\u001b[0m  \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m65\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m66\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m67\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m68\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m69\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m70\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m71\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m72\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m73\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m74\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m75\u001b[0m: \u001b[1;36m0\u001b[0m  \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m76\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m77\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m78\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m79\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m80\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m81\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m82\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m83\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m84\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m85\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m86\u001b[0m: \u001b[1;36m0\u001b[0m  \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m87\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m88\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m89\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m90\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m91\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m92\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m93\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m94\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m95\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m96\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m97\u001b[0m: \u001b[1;36m1\u001b[0m  \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m98\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m99\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m100\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m101\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m102\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m103\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m104\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m105\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m106\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m107\u001b[0m: \u001b[1;36m0\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m108\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m109\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m110\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m111\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m112\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m113\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m114\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m115\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m116\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m117\u001b[0m:\n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;36m118\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m119\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m120\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m121\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m122\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m123\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m124\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m125\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m126\u001b[0m: \u001b[1;36m1\u001b[0m   \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m127\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m128\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m129\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m130\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m131\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m132\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m133\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m134\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m135\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m136\u001b[0m:\n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m0\u001b[0m \u001b[1;36m137\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m138\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m139\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m140\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m141\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m142\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m143\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m144\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m145\u001b[0m: \u001b[1;36m1\u001b[0m   \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m146\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m147\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m148\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m149\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m150\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m151\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m152\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m153\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m154\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m155\u001b[0m:\n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m0\u001b[0m \u001b[1;36m156\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m157\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m158\u001b[0m: \u001b[1;36m0\u001b[0m \u001b[1;36m159\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m160\u001b[0m: \u001b[1;36m1\u001b[0m \u001b[1;36m161\u001b[0m: \u001b[1;36m0\u001b[0m                        \n",
       "\u001b[2;36m             \u001b[0m                                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = design_space.run(fn_pre, fn_post, verbose=True)\n",
    "df = results.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b67c427",
   "metadata": {},
   "source": [
    "## Optimization and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296adb3a",
   "metadata": {},
   "source": [
    "The optimization prints the best fitness achieved in after the generation has been computed. We can further visualize this improvement using the optimizer object stored in the ``results``. After the optimization is complete, we see that we achieved the best fitness (reflection) of 0.897."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8dfdc377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitness value of the best solution = 0.912\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the best fitness over generations\n",
    "ga_instance = results.optimizer\n",
    "best_fitness = ga_instance.best_solutions_fitness\n",
    "generations = range(len(best_fitness))\n",
    "\n",
    "plt.plot(generations, best_fitness)\n",
    "plt.title(\"Best Fitness over Generations\")\n",
    "plt.xlabel(\"Generation\")\n",
    "plt.ylabel(\"Best Fitness\")\n",
    "plt.show()\n",
    "\n",
    "pixels_final, solution_fitness, solution_idx = ga_instance.best_solution()\n",
    "print(f\"Fitness value of the best solution = {solution_fitness:.3f}\")\n",
    "\n",
    "def plot_fitness_evolution(data_frame, solutions_per_pop, fitness_column_name=\"output\"):\n",
    "    \"\"\"Plot the best, worst, and average fitness across generations.\"\"\"\n",
    "\n",
    "    # Divide the solutions into generations\n",
    "    fitness_values = data_frame[fitness_column_name]\n",
    "    generation_fitness = [fitness_values[i: i + solutions_per_pop] for i in range(0, len(fitness_values), solutions_per_pop)]\n",
    "\n",
    "    # Get min, max, and mean values per generation\n",
    "    min_fitness = [generation.min() for generation in generation_fitness]\n",
    "    max_fitness = [generation.max() for generation in generation_fitness]\n",
    "    mean_fitness = [generation.mean() for generation in generation_fitness]\n",
    "\n",
    "    plt.plot(min_fitness, label=\"Worst fitness\", alpha=0.5)\n",
    "    plt.plot(max_fitness, label=\"Best fitness\", alpha=0.5)\n",
    "    plt.plot(mean_fitness, label=\"Average fitness\", alpha=0.5)\n",
    "    plt.xlabel(\"Generation\")\n",
    "    plt.ylabel(\"Fitness\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "plot_fitness_evolution(df, 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd16ffaf",
   "metadata": {},
   "source": [
    "Now that we have an optimized design, we will grab the best pixel array and re-simulate the design with a broadband [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) and a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) at the central wavelength. This final simulation can be defined by copying the original simulation and updating the monitors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2024c056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:38:04 BST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'final design'</span> with task_id                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-b96bb239-29e0-4358-ad9a-b87283aa576e'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:38:04 BST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'final design'\u001b[0m with task_id                           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-b96bb239-29e0-4358-ad9a-b87283aa576e'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">0-4358-ad9a-b87283aa576e'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=398467;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=472845;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=398467;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=192710;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=398467;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[32m-b96bb239-29e\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=398467;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[32m0-4358-ad9a-b87283aa576e'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c3897939d2a458aaa5104693b0be4df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:38:07 BST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:38:07 BST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "954b767ae3e944718b14aa620e426c91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:38:23 BST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:38:23 BST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:38:26 BST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:38:26 BST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:38:36 BST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">48</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:38:36 BST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m48\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:38:37 BST </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:38:37 BST\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:38:38 BST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:38:38 BST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:38:39 BST </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">0-4358-ad9a-b87283aa576e'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:38:39 BST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=540161;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=269756;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=540161;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=951144;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=540161;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[4;34m-b96bb239-29e\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=540161;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b96bb239-29e0-4358-ad9a-b87283aa576e\u001b\\\u001b[4;34m0-4358-ad9a-b87283aa576e'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:38:41 BST </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:38:41 BST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_final = make_sim(pixels_final)\n",
    "\n",
    "# Update mode monitor's recording frequencies\n",
    "mode_monitor = sim.monitors[0].copy(update={\"freqs\": freqs})\n",
    "\n",
    "# Add a field monitor to visualize field distribution\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, t / 2), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
    ")\n",
    "\n",
    "# Update simulation to use the new monitors\n",
    "sim_final = sim_final.copy(update={\"monitors\": [mode_monitor, field_monitor]})\n",
    "\n",
    "# Submit the simulation to the server\n",
    "sim_data_final = web.run(simulation=sim_final, task_name=\"final design\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "645d4052",
   "metadata": {},
   "source": [
    "After the simulation, we visualize the electric field norm at the central wavelength. As expected, a strong reflection is observed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0eff6f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_final.plot_field(\"field\", \"Ey\", \"abs\", vmin=0, vmax=1e2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89311128",
   "metadata": {},
   "source": [
    "Finally plot the reflection spectrum, where we see a broadband reflection. Considering the very compact footprint, the performance is very good. With this reflector design, we can further construct other devices such as high-quality-factor Fabry–Perot cavities as demonstrated in the [publication](https://doi.org/10.1364/PRJ.5.000B15)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20e9667f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R = abs(sim_data_final[\"mode\"].amps.sel(direction=\"-\").squeeze(drop=True).values) ** 2\n",
    "\n",
    "plt.plot(ldas, R, \"red\", linewidth=2)\n",
    "plt.xlim(min(ldas), max(ldas))\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel(\"Wavelength (µm)\")\n",
    "plt.ylabel(\"Reflectivity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf44ce8",
   "metadata": {},
   "source": [
    "With the optimized design, we can directly export a GDS file of the reflector for fabrication. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26cf87f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the misc/ directory to store the GDS file if it doesnt exist already\n",
    "import os\n",
    "if not os.path.exists('./misc/'):\n",
    "    os.mkdir('./misc/')\n",
    "    \n",
    "sim_final.to_gds_file(fname=\"misc/optimized_reflector.gds\", z=t / 2)"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to use genetic algorithm (GA) to optimize an on-chip reflector.",
  "feature_image": "./img/optimized_reflector.png",
  "features": [
   "Global optimization"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "genetic algorithm, optimization, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "title": "Genetic Algorithm Optimization of a Reflector | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}