94d7c52666
Mostly added comments, but also fixed a small bug in parent selection where the tournament size would be much smaller than it should be.
57 lines
3.5 KiB
Python
57 lines
3.5 KiB
Python
import random
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from initialization.chromosome_structure.chromosome import Chromosome as create_chromosome
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from initialization.gene_structure.gene import Gene as create_gene
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from initialization.population_structure.population import Population
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from initialization.chromosome_structure.chromosome import Chromosome
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class Parent_Selection:
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class Tournament:
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def with_replacement(ga):
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tournament_size = int(len(ga.population.get_all_chromosomes())*ga.tournament_size_ratio)
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if tournament_size < 5:
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tournament_size = 5
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# Probability used for determining if a chromosome should enter the mating pool.
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selection_probability = ga.selection_probability
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# Repeat tournaments until the mating pool is large enough.
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while (len(ga.population.mating_pool) < len(ga.population.get_all_chromosomes())*ga.parent_ratio):
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# Generate a random tournament group and sort by fitness.
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tournament_group = ga.sort_by_best_fitness([random.choice(ga.population.get_all_chromosomes()) for n in range(tournament_size)])
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# For each chromosome, add it to the mating pool based on its rank in the tournament.
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for index in range(tournament_size):
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# Probability required is selection_probability * (1-selection_probability) ^ (tournament_size-index+1)
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# e.g. top ranked fitness has probability: selection_probability
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# second ranked fitness has probability: selection_probability * (1-selection_probability)
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# third ranked fitness has probability: selection_probability * (1-selection_probability)^2
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# etc.
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if random.uniform(0, 1) < selection_probability * pow(1-selection_probability, index):
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ga.population.mating_pool.append(tournament_group[index])
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class Roulette:
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def roulette_selection(ga):
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"""Roulette selection works based off of how strong the fitness is of the
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chromosomes in the population. The stronger the fitness the higher the probability
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that it will be selected. Using the example of a casino roulette wheel.
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Where the chromosomes are the numbers to be selected and the board size for
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those numbers are directly proportional to the chromosome's current fitness. Where
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the ball falls is a randomly generated number between 0 and 1"""
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total_fitness = sum(ga.population.chromosome_list[i].get_fitness() for i in range(len(ga.population.chromosome_list)))
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rel_fitnesses = []
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for chromosome in ga.population.chromosome_list:
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if (total_fitness != 0):
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rel_fitnesses.append(float(chromosome.fitness)/total_fitness)
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probability = [sum(rel_fitnesses[:i+1]) for i in range(len(rel_fitnesses))]
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while (len(ga.population.mating_pool) < len(ga.population.get_all_chromosomes())*ga.parent_ratio):
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rand_number = random.random()
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# Loop through the list of probabilities
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for i in range(len(probability)):
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# If the probability is greater than the random_number, then select that chromosome
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if (probability[i] >= rand_number):
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ga.population.mating_pool.append(ga.population.chromosome_list[i])
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# print (f'Selected chromosome : {i}')
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break |