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Minimilistic code for conducting genetic optimisation. Two parents and singe-point crossover. Tournament selection.
import random
def create_child(population, genes, tournament_size, mutation_rate):
# Breed two parents, returning (possibly mutated) offspring
parent1 = select(population, tournament_size)
parent2 = select(population, tournament_size)
child = crossover(parent1, parent2)
child = mutate(child, mutation_rate, genes)
return child
def select(population, tournament_size):
# Choose an individual in population through winner of a tournament
assert tournament_size <= len(population)
tournament_winner_index = min(random.sample(range(len(population)), tournament_size))
parent = population[tournament_winner_index]
return parent.copy()
def crossover(parent1,parent2):
# Returns results of single point crossover of the parents
crossover_point = random.choice(range(len(parent1)))
child = parent1.copy()
child[crossover_point:] = parent2[crossover_point:]
return child
def mutate(child, mutation_rate, genes):
# Choose to replace a gene with a random different one
for gene in range(len(child)):
if random.random() < mutation_rate:
child[gene] = random.choice(genes)
return child
def population_fitnesses(population, fitness_func):
# Takes list of individuals and returns population (sorted by fitness) and fitness lists
fitnesses = [fitness_func(i) for i in population]
fitnesses, population = zip(*sorted(zip(fitnesses, population), reverse=True))
return list(population), list(fitnesses)
def optimise(iterations, genes, genome_length, fitness_func
, population_size, elites_size, tournament_size, mutation_rate, max_fitness=None):
# Initialisation and order by fitnesses
population = [random.choices(genes, k=genome_length) for _ in range(population_size)]
population, fitnesses = population_fitnesses(population, fitness_func)
# Iterate through generations of populations
for generation in range(iterations):
new_population = [p.copy() for p in population]
# Keep the best elites, replace rest of population with new children
for p in range(elites_size, population_size):
new_population[p] = create_child(population, genes, tournament_size, mutation_rate)
# Compute fitnesses and sort population by them
population, fitnesses = population_fitnesses(new_population, fitness_func)
print(f"Gen: {generation}, Best: {population[0]} with Fitness: {fitnesses[0]}")
# Check if we've passed the max fitness stopping condition
if max_fitness is not None and fitnesses[0] >= max_fitness:
break
return population[0], fitnesses[0]
if __name__ == "__main__":
iterations = 50
population_size = 100
elites_size = 5
tournament_size = 4
mutation_rate = 0.1
genes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
genome_length = 10
fitness_func = lambda x: sum(x)
best, best_fitness = optimise(iterations, genes, genome_length, fitness_func
, population_size, elites_size, tournament_size, mutation_rate, 90)
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