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evolution.py
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evolution.py
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import numpy as np
from utils.read_and_write_constraints import retrieve_constraints
from utils.population_creation import generate_population, Population
from utils.crossover_functions import crossover
def run_evolution(population_size, amount_iterations, mutation_rate):
problem_parameters = retrieve_constraints()
population = generate_population(population_size, problem_parameters)
for iteration in range(amount_iterations):
# Calculate fitness
population.fitness()
# print(f"{population}\n{population.fitness_values}")
print(f"iteration {iteration} average fitness: {np.mean(population.fitness_values)} max fitness: {np.max(population.fitness_values)}\n")
# The new population
new_population = Population()
for i in range(population_size):
# Selection
parent_1, parent_2 = population.choose_parents()
# Reproduction
offspring = crossover(parent_1, parent_2)
offspring.mutate(mutation_rate)
# print(f"Offspring is {offspring}")
new_population.add_individual(offspring)
population = new_population
# print(population)
return population, problem_parameters
if __name__ == "__main__":
final_population = run_evolution(population_size=500,
amount_iterations=100,
mutation_rate=0.01)