Several changes for smoother results and more control over the NN.

examples/neuroevolution-steering/sketch.js

@@ -28,7 +28,7 @@ let foodBuffer = 50;
 // How many sensors does each vehicle have?
 let totalSensors = 8;
 // How far can each vehicle see?
-let sensorLength = 150;
+let sensorLength = 50;
 // What's the angle in between sensors
 let sensorAngle = (Math.PI * 2) / totalSensors;
 
@@ -94,14 +94,19 @@ function draw() {
     if (population.length < 20) {
       for (let v of population) {
         // Every vehicle has a chance of cloning itself according to score
-        // Argument to "clone" is probability
-        let newVehicle = v.clone(0.1 * v.score / record);
+        let probability = 0.1 * v.score / record;
+        let newVehicle = v.clone(probability);
         // If there is a child
         if (newVehicle != null) {
           population.push(newVehicle);
         }
       }
     }
+    // Make sure we never run out of vehicles, but favor reproduction
+    if (population.length <= 2) {
+      let vehicle = new Vehicle();
+      population.push(vehicle);
+    }
   }
 
   // Draw all the food

examples/neuroevolution-steering/vehicle.js

@@ -4,17 +4,9 @@
 
 // Evolutionary "Steering Behavior" Simulation
 
-
-// Mutation function to be passed into Vehicle's brain
-function mutate(x) {
-  if (random(1) < 0.1) {
-    let offset = randomGaussian() * 0.5;
-    let newx = x + offset;
-    return newx;
-  } else {
-    return x;
-  }
-}
+// Neural Network parameters
+// Mutation rate is set quite high because there is no crossover
+const mutationRate = 0.25;
 
 // This is a class for an individual sensor
 // Each vehicle will have N sensors
@@ -37,7 +29,7 @@ class Vehicle {
     this.velocity = createVector();
     this.position = createVector(random(width), random(height));
     this.r = 4;
-    this.maxforce = 0.1;
+    this.maxforce = 0.2;
     this.maxspeed = 4;
     this.minspeed = 0.25;
     this.maxhealth = 3;
@@ -54,7 +46,7 @@ class Vehicle {
     // If a brain is passed via constructor copy it
     if (brain) {
       this.brain = brain.copy();
-      this.brain.mutate(mutate);
+      this.mutate(mutationRate);
       // Otherwise make a new brain
     } else {
       // inputs are all the sensors plus position and velocity info
@@ -64,10 +56,28 @@ class Vehicle {
       this.brain = new NeuralNetwork(inputs, 32, 2);
     }
 
-    // Health keeps vehicl alive
+    // Health keeps vehicle alive
     this.health = 1;
   }
 
+  mutate(rate) {
+    // Check if this should be mutated at all
+    if (Math.random() < rate) {
+      // This is how we adjust weights ever so slightly
+      function mutate(x) {
+        // Mutate only so much of the values
+        if (Math.random() < rate) {
+          var offset = randomGaussian() * 0.5;
+          // var offset = random(-0.1, 0.1);
+          var newx = x + offset;
+          return newx;
+        } else {
+          return x;
+        }
+      }
+      this.brain.mutate(mutate);
+    }
+  }
 
   // Called each time step
   update() {
@@ -147,11 +157,12 @@ class Vehicle {
 
     // Create inputs
     let inputs = [];
-    // This is goofy but these 4 inputs are mapped to distance from edges
-    inputs[0] = constrain(map(this.position.x, foodBuffer, 0, 0, 1), 0, 1);
-    inputs[1] = constrain(map(this.position.y, foodBuffer, 0, 0, 1), 0, 1);
-    inputs[2] = constrain(map(this.position.x, width - foodBuffer, width, 0, 1), 0, 1);
-    inputs[3] = constrain(map(this.position.y, height - foodBuffer, height, 0, 1), 0, 1);
+    // These inputs are the location of the vehicle
+    inputs[0] = this.position.x / width;
+    inputs[1] = this.position.y / height;
+    // These inputs are the distance of the vehicle to east- and west borders
+    inputs[2] = 1 - inputs[0];
+    inputs[3] = 1 - inputs[1];
     // These inputs are the current velocity vector
     inputs[4] = this.velocity.x / this.maxspeed;
     inputs[5] = this.velocity.y / this.maxspeed;