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#! /usr/bin/env python
Very simple demo in which organisms try to minimise
the output value of a function
from pygene.gene import FloatGene, FloatGeneMax
from pygene.organism import Organism, MendelOrganism
from pygene.population import Population
class CvGene(FloatGeneMax):
Gene which represents the numbers used in our organism
# genes get randomly generated within this range
randMin = -100.0
randMax = 100.0
# probability of mutation
mutProb = 0.1
# degree of mutation
mutAmt = 0.1
class Converger(MendelOrganism):
Implements the organism which tries
to converge a function
genome = {'x':CvGene, 'y':CvGene}
def fitness(self):
Implements the 'fitness function' for this species.
Organisms try to evolve to minimise this function's value
return self['x'] ** 2 + self['y'] ** 2
def __repr__(self):
return "<Converger fitness=%f x=%s y=%s>" % (, self['x'], self['y'])
# create an empty population
pop = Population(species=Converger, init=2, childCount=50, childCull=20)
# now a func to run the population
def main():
while True:
# execute a generation
# get the fittest member
best =
# and dump it out
print best
except KeyboardInterrupt:
if __name__ == '__main__':