def evolve(world: World, population: Population):
new_generation = Population(population.population_size, world.gene_length,
False, [])
elitism_number = population.population_size // 2
#Elitism
for _ in range(elitism_number):
fittest = population.getFittest()
new_generation.addIndividual(fittest)
population.removeIndividual(fittest)
#Crossover
for _ in range(elitism_number, population.population_size):
parent1 = selection(new_generation, world)
parent2 = selection(new_generation, world)
child = crossover(parent1, parent2)
child.setFitness(
fitnessFN(world, child.path, world.startX, world.startY,
world.foodCount))
new_generation.addIndividual(child)
#Mutation
for i in range(elitism_number, population.population_size):
mutation(new_generation.individuals[i], world.mutation_rate)
return new_generation
def run(args):
startX = args.startx
startY = args.starty
foodCount = args.food_count
world = World(args.world_size, args.startx, args.starty, args.food_count,
args.mut_rate)
#Generating the Population
population = Population(args.population_size, world.gene_length, True, [])
start_time = time.time()
elapsed_time = 0
total_fitness = 0
max_fitness = 0
max_fitness_index = 0
i = 0
for individual in population.individuals:
fit = fitnessFN(world, individual.path, startX, startY, foodCount)
individual.setFitness(fit)
total_fitness += fit
if fit > max_fitness:
max_fitness = fit
max_fitness_index = i
i += 1
best_individual = copy.copy(population.individuals[max_fitness_index])
avg_fitness = total_fitness / population.population_size
total_generations = args.max_generation_number
generation_counter = 0
while generation_counter < total_generations and best_individual.fitness < world.foodCount:
print("Generation : ", generation_counter)
print("Max fitness: ", best_individual.fitness)
print("Average fitness: ", avg_fitness)
population = evolve(world, population)
new_best_individual = population.getFittest()
avg_fitness = population.calculateAverageFitness()
if new_best_individual.fitness > best_individual.fitness:
generation_counter, best_individual = 0, new_best_individual
else:
generation_counter += 1
total_generations = 0
end_time = time.time()
elapsed_time = end_time - start_time
print("\n-------------------------------\nElapsed Time: ", elapsed_time,
" sec")
print("Population Size: ", args.population_size, " World Size: ",
args.world_size, "\nStart x, y:", args.startx, ",", args.starty,
" Food Count: ", args.food_count, "\nMutation Rate: ", args.mut_rate)
print("Result: ", best_individual.toString())
matrix = traverse(best_individual.path, world)
draw_matrix(matrix, 'title')