init = bd.get_init(maze)
position, blocked, walked_open, returned = bd.walk(individual, maze)
distance_end = math.ceil(
math.sqrt(((position[0] - end[0])**2) + ((position[1] - end[1])**2)))
distance_init = math.ceil(
math.sqrt(((position[0] - init[0])**2) + ((position[1] - init[1])**2)))
# print(
# 1000 - distance_init * (blocked + returned - walked_open),
# blocked,
# position[0],
# position[1],
# )
return (individual, max_fitness - distance_end - blocked - returned)
if __name__ == "__main__":
maze = bd.read()
result = gp.run(
partial(fitness, maze),
mutation_function=mt.random_replacement,
crossover_function=cr.uniform,
select_function=sl.stochastic_tournament,
config_file="maze.cfg",
)
# convergence_chart(result)
maze_result = bd.simulate(result["champion"][0], maze)
print(result["champion"][0])
bd.fig(maze_result, "./out/maze_result.png")
rl, rs = normalization(ind)
r = -1
FOn = (30 * rs + 40 * rl) / 1360
Hn = max(0, (rs + 2 * rl - 40)) / 16
fit = FOn + r * Hn
return ind, fit
if __name__ == "__main__":
result = gp.run(
fitness,
mutation_function=mt.bit_flip,
crossover_function=cr.two_points,
select_function=sl.fitness_proportionate_selection,
config_file="radio.cfg",
phenotype=show,
)
convergence_chart(result, False)
plot_runs(result)
"""
conjunto: radio standart, luxo
dominio:
rs [0-24] -> 5 bits
rl [0-16] -> 5 bits
cromosomo: 10bits
rs + 2rl =< 40
return individual, (fit * 0.8 + profit * 0.2) / max_fit
if __name__ == "__main__":
parse("p_queens.cfg")
array_length = int(environ.get("DIM"))
profit_array = np.array(list(map(float, range(1, array_length**2 + 1))))
op, po = math.sqrt, math.log10
for i in range(len(profit_array)):
profit_array[i] = op(profit_array[i])
if not (i + 1) % array_length:
po, op = op, po
continue
max_fit_profit = sum(sorted(profit_array)[-1:-array_length - 1:-1])
# print(fitness(np.array([7, 5, 3, 1, 6, 8, 2, 4])))
result = gp.run(
fitness,
mutation_function=mt.swap,
crossover_function=cr.pmx,
select_function=sl.stochastic_tournament,
config_file="p_queens.cfg",
phenotype=show_table,
pool_size=4,
)
convergence_chart(result, False)
plot_runs(result)