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ga.py
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ga.py
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# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
from pswarm_py import pswarm
from numpy import zeros
from numpy import ones
from aux import *
from oldtrip import *
from numpy.random import normal, uniform
popsize = 49
select, iters = round(math.sqrt(popsize)), round(5000 / popsize)
def select_fittest(distr):
"""Randomly select an element according to the given distribution."""
i = 0
v = uniform()
while i < len(distr) and v > distr[i]:
i += 1
return i
def crossover(a, b):
"""Sibling is an average of parents."""
c = []
for i in range(0, len(a)):
c.append(((a[i][0] + b[i][0]) / 2, (a[i][1] + b[i][1]) / 2))
return c
def ga_distortion(trip, testset_xy):
# Generate initial population.
new_probings = []
for i in range(0, popsize):
new_probings.append(trip.xys.copy())
for it in range(0, iters):
probings = new_probings.copy()
# Mutate population and calculate fitness information.
fit = []
for i in range(0, popsize):
v, probings[i] = trip.fitness(probings[i], testset_xy, random_distortion)
fit.append(v)
# Define a probability distribution to select the fittest ones.
minfit = min(fit)
for i in range(0, popsize):
fit[i] -= minfit
sumfit = sum(fit)
distr = []
for i in range(0, popsize):
fit[i] /= sumfit
for i in range(1, popsize):
distr.append(sum(fit[:i]))
distr.append(1)
# Select the fittest ones.
selected = set()
while len(selected) < select:
selected.add(select_fittest(distr))
# Crossover
for i in selected:
for j in selected:
new_probings.append(crossover(probings[i], probings[j]))
# Adopt best individual.
vmin = 999999
for i in range(0, popsize):
v, _ = trip.fitness(probings[i], testset_xy)
if v < vmin:
best = i
vmin = v
trip.xys = probings[best].copy()
return vmin