Source code for infpy.genepy.population

# Copyright John Reid 2007

from random import random
from itertools import chain

[docs]class Population(object): """ A collection of individuals of a particular species """ species = None # defines initialisation, reproduction, and mutation fitness_fn = None # scores an individual minimise = True # minimise or maximise the fitness? breeding_proportion = .1 # proportion of population selected as parents carry_over_parents = True # include parents in next generation? p_mutation = .1 # chance of mutation of an individual at each generation post_generation_process = None # function to be called at end of every generation def __init__( self, size, species, fitness_fn ): self.species = species self.fitness_fn = fitness_fn self.individuals = [ self.species.random_individual() for i in xrange(size) ]
[docs] def generation(self): "Run through a generation" self.score_individuals() self.select_parents() self.reproduce() self.combine_generations() self.mutate() if self.post_generation_process: self.post_generation_process(self)
[docs] def score_individuals(self): for i in self.individuals: = self.fitness_fn(i)
[docs] def select_parents(self): self.individuals.sort(key=lambda x:, reverse=not self.minimise) self.most_fit = self.individuals[0] num_parents = int(self.breeding_proportion*len(self.individuals)) self.parents = self.individuals[:num_parents]
[docs] def reproduce(self): # how many children if self.carry_over_parents: num_children = len(self.individuals) - len(self.parents) else: num_children = len(self.individuals) from random import choice self.children = [ self.species.mate(choice(self.parents), choice(self.parents)) for i in xrange(num_children) ]
[docs] def combine_generations(self): self.individuals = list(chain(self.parents,self.children))
[docs] def mutate(self): for i in self.individuals: if random() < self.p_mutation: self.species.mutate(i)