00001 """Implement Roulette Wheel selection on a population. This implements Roulette Wheel selection in which individuals are selected from a population randomly, with their proportion of selection based on their relative fitness in the population. """ # standard modules import random import copy # local modules from Abstract import AbstractSelection 00014 class RouletteWheelSelection(AbstractSelection): """Roulette wheel selection proportional to individuals fitness. The implements a roulette wheel selector that selects individuals from the population, and performs mutation and crossover on the selected individuals. """ 00021 def __init__(self, mutator, crossover, repairer = None): """Initialize the selector. Arguments: o mutator -- A Mutation object which will perform mutation on an individual. o crossover -- A Crossover object which will take two individuals and produce two new individuals which may have had crossover occur. o repairer -- A class which can do repair on rearranged genomes to eliminate infeasible individuals. If set at None, so repair will be done. """ AbstractSelection.__init__(self, mutator, crossover, repairer) 00039 def select(self, population): """Perform selection on the population based using a Roulette model. Arguments: o population -- A population of organisms on which we will perform selection. The individuals are assumed to have fitness values which are due to their current genome. """ # set up the current probabilities for selecting organisms # from the population prob_wheel = self._set_up_wheel(population) probs = prob_wheel.keys() probs.sort() # now create the new population with the same size as the original new_population = [] for pair_spin in range(len(population) / 2): # select two individuals using roulette wheel selection choice_num_1 = random.random() choice_num_2 = random.random() # now grab the two organisms from the probabilities chosen_org_1 = None chosen_org_2 = None prev_prob = 0 for cur_prob in probs: if choice_num_1 > prev_prob and choice_num_1 <= cur_prob: chosen_org_1 = prob_wheel[cur_prob] if choice_num_2 > prev_prob and choice_num_2 <= cur_prob: chosen_org_2 = prob_wheel[cur_prob] prev_prob = cur_prob assert chosen_org_1 is not None, "Didn't select organism one" assert chosen_org_2 is not None, "Didn't select organism two" # do mutation and crossover to get the new organisms new_org_1, new_org_2 = self.mutate_and_crossover(chosen_org_1, chosen_org_2) new_population.extend([new_org_1, new_org_2]) return new_population 00085 def _set_up_wheel(self, population): """Set up the roulette wheel based on the fitnesses. This creates a fitness proportional 'wheel' that will be used for selecting based on random numbers. Returns: o A dictionary where the keys are the 'high' value that an individual will be selected. The low value is determined by the previous key in a sorted list of keys. For instance, if we have a sorted list of keys like: [.1, .3, .7, 1] Then the individual whose key is .1 will be selected if a number between 0 and .1 is chosen, the individual whose key is .3 will be selected if the number is between .1 and .3, and so on. The values of the dictionary are the organism instances. """ # first sum up the total fitness in the population total_fitness = 0 for org in population: total_fitness += org.fitness # now create the wheel dictionary for all of the individuals wheel_dict = {} total_percentage = 0 for org in population: org_percentage = float(org.fitness) / float(total_fitness) # the organisms chance of being picked goes from the previous # percentage (total_percentage) to the previous percentage # plus the organisms specific fitness percentage wheel_dict[total_percentage + org_percentage] = copy.copy(org) # keep a running total of where we are at in the percentages total_percentage += org_percentage return wheel_dict

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