e05aa7f62b
Instead of a nested approach, selection/crossover/mutation are all called separately and directly by the GA. selection_impl was also separated into parent_selection_impl and survivor_selection_impl, as both are needed separately.
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
class Population:
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# fitness = Empty; population = [chromosome, chromosome, etc.]
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def __init__(self, chromosomes = None):
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if chromosomes is None:
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self.chromosomes = []
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else:
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self.chromosomes = chromosomes
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self.fitness = None
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def get_closet_fitness(self,value):
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# Get the chomosome that has the closets fitness to the value defined
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pass
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def add_chromosome(self, chromosome, index = -1):
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if index == -1:
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index = len(self.chromosomes)
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self.chromosomes.insert(index, chromosome)
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def remove_chromosome(self, index):
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del self.chromosomes[index]
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def get_all_chromosomes(self):
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"""returns all chromosomes in the population"""
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return self.chromosomes
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def get_fitness(self):
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return self.fitness
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def set_all_chromosomes(self, chromosomes):
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self.chromosomes = chromosomes
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def set_chromosome(self, chromosome, index = -1):
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if index == -1:
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index = len(self.chromosomes)-1
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self.chromosomes[index] = chromosome
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def set_fitness(self, fitness):
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self.fitness = fitness
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def __repr__(self):
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for index in range(len(self.chromosomes)):
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return f'{self.chromosomes[index]}'
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def print_all(self):
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# Ex .Current population
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# Chromosome 1 - [gene][gene][gene][.etc] / Chromosome fitness - #
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print("Current population:")
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for index in range(len(self.chromosomes)):
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print(f'Chromosome - {index} {self.chromosomes[index]}', end = "")
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print(f' / Fitness = {self.chromosomes[index].fitness}')
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def generate_first_chromosomes(self, chromosome_count, chromosome_length, gene_lower_bound, gene_upper_bound):
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#Creating the chromosomes with Genes of random size
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for x in range(chromosome_count):
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chromosome = Chromosome(chromosome_length)
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for y in range(chromosome_length):
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chromosome.gene_set[y] = Gene(random.randint(gene_lower_bound[y], gene_upper_bound[y]))
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self.chromosome_set.append(chromosome)
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