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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,98 @@ | ||
| import random | ||
| from typing import List, Tuple | ||
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| from src.genome import Genome, Innovation | ||
| from src.species import Species | ||
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| class GeneticAlgorithm: | ||
| def __init__(self, population_size: int, num_inputs: int, num_outputs: int): | ||
| self.population_size = population_size | ||
| self.num_inputs = num_inputs | ||
| self.num_outputs = num_outputs | ||
| self.population: List[Genome] = [] | ||
| self.species_list: List[Species] = [] | ||
| self.innovation = Innovation.get_instance() | ||
| self.genome_id_counter = 0 | ||
| self.species_id_counter = 0 | ||
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| self.initialize_population() | ||
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| def initialize_population(self): | ||
| for _ in range(self.population_size): | ||
| genome = Genome( | ||
| genome_id=self.genome_id_counter, | ||
| num_inputs=self.num_inputs, | ||
| num_outputs=self.num_outputs, | ||
| ) | ||
| self.population.append(genome) | ||
| self.genome_id_counter += 1 | ||
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| def evaluate_fitness(self, evaluate_function): | ||
| for genome in self.population: | ||
| genome.fitness = evaluate_function(genome) | ||
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| def speciate(self, compatibility_threshold: float): | ||
| self.species_list = [] | ||
| for genome in self.population: | ||
| found_species = False | ||
| for species in self.species_list: | ||
| representative = species.members[0] | ||
| distance = genome.compute_compatibility_distance(representative) | ||
| if distance < compatibility_threshold: | ||
| species.add_member(genome) | ||
| genome.species = species.species_id | ||
| found_species = True | ||
| break | ||
| if not found_species: | ||
| new_species = Species(self.species_id_counter) | ||
| new_species.add_member(genome) | ||
| genome.species = new_species.species_id | ||
| self.species_list.append(new_species) | ||
| self.species_id_counter += 1 | ||
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| def adjust_fitness(self): | ||
| for species in self.species_list: | ||
| species.adjust_fitness() | ||
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| def reproduce(self): | ||
| new_population = [] | ||
| total_average_fitness = sum( | ||
| species.average_fitness for species in self.species_list | ||
| ) | ||
| for species in self.species_list: | ||
| offspring_count = int( | ||
| (species.average_fitness / total_average_fitness) * self.population_size | ||
| ) | ||
| for _ in range(offspring_count): | ||
| parent1 = species.select_parent() | ||
| if random.random() < 0.25: | ||
| # Mutation without crossover | ||
| child = parent1.copy() | ||
| child.mutate() | ||
| else: | ||
| parent2 = species.select_parent() | ||
| child = parent1.crossover(parent2) | ||
| child.mutate() | ||
| child.genome_id = self.genome_id_counter | ||
| self.genome_id_counter += 1 | ||
| new_population.append(child) | ||
| # If we don't have enough offspring due to rounding, fill up the population | ||
| while len(new_population) < self.population_size: | ||
| parent = random.choice(self.population) | ||
| child = parent.copy() | ||
| child.mutate() | ||
| child.genome_id = self.genome_id_counter | ||
| self.genome_id_counter += 1 | ||
| new_population.append(child) | ||
| self.population = new_population | ||
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| def evolve( | ||
| self, generations: int, evaluate_function, compatibility_threshold: float | ||
| ): | ||
| for generation in range(generations): | ||
| print(f"Generation {generation+1}") | ||
| self.evaluate_fitness(evaluate_function) | ||
| self.speciate(compatibility_threshold) | ||
| self.adjust_fitness() | ||
| self.reproduce() |
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