def crossover_fitness_driven_one_point(p1, p2):
point = random.randint(1, len(p1.gene_list) - 1)
c1, c2 = copy.deepcopy(p1.gene_list), copy.deepcopy(p2.gene_list)
c1[point:], c2[point:] = p2.gene_list[point:], p1.gene_list[point:]
child1 = Individual(c1)
child2 = Individual(c2)
candidates = [child1, child2, p1, p2]
best = sorted(candidates, key=lambda ind: ind.fitness, reverse=True)
return best[0:2]
def mutation_fitness_driven_bit_flip(ind, max_tries=3):
for t in range(0, max_tries):
mut = copy.deepcopy(ind.gene_list)
pos = random.randint(0, len(ind.gene_list) - 1)
g1 = mut[pos]
mut[pos] = (g1 + 1) % 2
mutated = Individual(mut)
if mutated.fitness > ind.fitness:
return mutated
return ind
import random
from ch9.knapsack.individual import Individual
from ch9.knapsack.random_set_generator import random_set_generator
if __name__ == '__main__':
random.seed(1)
items = random_set_generator(1, 100, 0.1, 7, 200)
Individual.set_items(items)
Individual.set_max_weight(10)
population = [Individual.create_random() for _ in range(1000)]
average_weight = sum([ind.total_weight()
for ind in population]) / len(population)
print(f'Average weight of population: {average_weight}')
def mutate(ind):
mutated_gene = mutation_bit_flip(ind.gene_list)
return Individual(mutated_gene)
from ch9.knapsack.random_set_generator import random_set_generator
from ch9.knapsack.toolbox import mutation_bit_flip
def mutate(ind):
mutated_gene = mutation_bit_flip(ind.gene_list)
return Individual(mutated_gene)
if __name__ == '__main__':
random.seed(1)
random.seed(63)
items = random_set_generator(1, 100, 0.1, 7, 200)
Individual.set_items(items)
Individual.set_max_weight(10)
gene_set = [0] * len(items)
inclusions = [2, 30, 34, 42, 48, 64, 85, 104, 113, 119, 157, 174]
for i in inclusions:
gene_set[i] = 1
ind = Individual(gene_set)
alive = 0
killed = 0
for _ in range(1000):
mutated = mutate(ind)
if mutated.fitness == 0:
killed += 1
def create_random_individual(gene_len, zeros=1, ones=1):
s = ([0] * zeros) + ([1] * ones)
return Individual([random.choice(s) for _ in range(gene_len)])
import random
from ch9.knapsack.individual import Individual
from ch9.knapsack.random_set_generator import random_set_generator
def create_random_individual(gene_len, zeros=1, ones=1):
s = ([0] * zeros) + ([1] * ones)
return Individual([random.choice(s) for _ in range(gene_len)])
if __name__ == '__main__':
random.seed(1)
items = random_set_generator(1, 100, 0.1, 7, 200)
Individual.set_items(items)
Individual.set_max_weight(10)
population = [
create_random_individual(len(items), 50, 1) for _ in range(1000)
]
average_weight = sum([ind.total_weight()
for ind in population]) / len(population)
print(f'Average weight of population: {average_weight}')
def crossover(parent1, parent2):
child1_genes, child2_genes = crossover_one_point(parent1.gene_list, parent2.gene_list)
return Individual(child1_genes), Individual(child2_genes)
stats,
plot_stats,
)
def crossover(parent1, parent2):
child1_genes, child2_genes = crossover_one_point(parent1.gene_list, parent2.gene_list)
return Individual(child1_genes), Individual(child2_genes)
def mutate(ind):
mutated_gene = mutation_bit_flip(ind.gene_list)
return Individual(mutated_gene)
Individual.set_items(get_items_from_my_room())
Individual.set_max_weight(10)
random.seed(63)
POPULATION_SIZE = 8
CROSSOVER_PROBABILITY = .7
MUTATION_PROBABILITY = .2
MAX_GENERATIONS = 20
first_population = [Individual.create_random() for _ in range(POPULATION_SIZE)]
population = first_population.copy()
fitness_list = [ind.fitness for ind in population]
fit_avg = [sum(fitness_list) / len(population)]
fit_best = [max(fitness_list)]
fit_best_ever = [max(fitness_list + fit_best)]
best_ind = random.choice(first_population)