Python implementation of Genetic Algorithm
Project description
pyga
Python implementation of Genetic Algorithm
Installation
pip3 install pygenal
Usage
#!/usr/bin/env python3
# import required classes
from ga import Population, Individual, Gene, geneTypes, Duration
import numpy as np
import time
# create population of your own, override fitness method to suit your needs.
# your system evaluation goes here, for sake of demonstration, use polynom
class Polynom(Population):
def fitness(self, individual):
x = individual.get("x").value
y = individual.get("y").value
color = individual.get("color").value
# test against function with global maximum:
# max{x + 3 x^2 - x^4 - y^6 + y/3 - y^3 4 - y - 5} ≈ 3.35864
# at (x, y) ≈ (1.30084, -1.27413)
# + favorize chocolate over shorter colors, just for demonstration
return (x + 3*(x**2) - x**4 - y**6 + y/3.0 - (y**3)*4 - y - 5) + len(color)/10
# introduce non-number options such as color, good for selections
colors = ["brown", "green", "grey", "blue", "chocolate"]
if __name__ == '__main__':
# spawn individual
i1 = Individual()
# construct "DNA"
i1 += Gene("x", geneTypes.REAL, np.random.randint(-3, 3), min=-10, max=10)
i1 += Gene("y", geneTypes.REAL, np.random.randint(-3, 3), min=-10, max=10)
i1 += Gene("color", geneTypes.VALUE, np.random.choice(colors), availableOptions=colors, dominant=True)
# create tribe with size of 100 individuals based on your first Individual
population = Polynom(
species=i1,
size=100,
chanceOfMutationStart=0.5,
chanceOfMutationStop=0.01
)
tStart = time.time()
# start evolution until:
# you didn't pass 1000 generations OR
# fittest didn't change for 200 generations OR
# 2.5s didn't passes yet
population.evolve(
generations=1000,
verbose=True,
timeout=Duration(seconds=2, miliseconds=500),
terminateAfter=200,
)
print(f"Evolved in {time.time()-tStart}s, precision: {(3.35864+len('chocolate')/10 - population.fittest.score)*100}%")
print(f"Fittest: {population.fittest}, genes: {repr(population.fittest)}")
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