Genetic Algorithm (GA) for NQueens¶

In [1]:

from metazoo.bio.evolutionary import GeneticAlgorithm
from metazoo.bio.evolutionary.operators import selection, mutation, crossover
from metazoo.gym.combinatorial import NQueens
from metazoo.bio.utils import encoding

from metazoo.bio.evolutionary import GeneticAlgorithm from metazoo.bio.evolutionary.operators import selection, mutation, crossover from metazoo.gym.combinatorial import NQueens from metazoo.bio.utils import encoding

In [2]:

N = 8
fitness_function = NQueens(n=N)
encoder = encoding.Permutation(permutation_size=N)

ga = GeneticAlgorithm(
    fitness_function=fitness_function,
    crossover_function=crossover.PMX,
    mutation_function=mutation.swap,
    selection_function=selection.tournament,
    population_size=200,
    mutation_rate=0.01,
    crossover_rate=0.7,
    encoder=encoder,
    elitism=0.1,
    minimize=False,
)

ga.run(generations=500)

N = 8 fitness_function = NQueens(n=N) encoder = encoding.Permutation(permutation_size=N) ga = GeneticAlgorithm( fitness_function=fitness_function, crossover_function=crossover.PMX, mutation_function=mutation.swap, selection_function=selection.tournament, population_size=200, mutation_rate=0.01, crossover_rate=0.7, encoder=encoder, elitism=0.1, minimize=False, ) ga.run(generations=500)

Output()

In [3]:

print(f'Best Individual: {ga.best_individual}')
print(f'Best Fitness: {ga.best_fitness}')

print(f'Best Individual: {ga.best_individual}') print(f'Best Fitness: {ga.best_fitness}')

Best Individual: [2 5 1 6 4 0 7 3]
Best Fitness: 28.0

In [4]:

ga.fitness_plot()

ga.fitness_plot()

In [5]:

fitness_function.plot(solution=ga.best_individual, attacks=fitness_function.attacks(ga.best_individual))

fitness_function.plot(solution=ga.best_individual, attacks=fitness_function.attacks(ga.best_individual))