# THE PROBLEM INSTANCE
# - optimization of ANN's weights is a COP
#++++++++++++++++++++++++++
ann_op_i = ANNOP(search_space=(-2, 2, n_weights), fitness_function=ann_i.stimulate,
minimization=False, validation_threshold=validation_threshold)
#++++++++++++++++++++++++++
# THE SEARCH
# restrictions:
# - 5000 offsprings/run max*
# - 50 offsprings/generation max*
# - use at least 5 runs for your benchmarks
# * including reproduction
#++++++++++++++++++++++++++
ga1 = GeneticAlgorithm2(ann_op_i, random_state, ps, uls.parametrized_tournament_selection(pressure),
uls.one_point_crossover, p_c, uls.parametrized_ball_mutation(radius), 0.2)
ga2 = GeneticAlgorithm(ann_op_i, random_state, ps, uls.parametrized_tournament_selection(pressure),
uls.two_point_crossover, p_c, uls.parametrized_ball_mutation(radius), 0.1)
sa1 = SimulatedAnnealing(ann_op_i, random_state, ps, uls.parametrized_ball_mutation(radius), control, update_rate)
search_algorithms = [ga1, ga2]
# initialize search algorithms
[algorithm.initialize() for algorithm in search_algorithms]
# execute search
[algorithm.search(n_iterations=n_gen, report=False) for algorithm in search_algorithms]
#++++++++++++++++++++++++++
# TEST
# - test algorithms on unseen data
#++++++++++++++++++++++++++
n_gen = 100
ps = 50
p_c = .7
p_m = 0.5
radius = .4
pressure = .2
ga1 = GeneticAlgorithm(
problem_instance=ann_op_i,
random_state=random_state,
population_size=ps,
selection=uls.parametrize_roulette_wheel_Wpressure_sharing_fitness(
pressure),
crossover=uls.geometric_semantic_crossover,
p_c=p_c,
mutation=uls.parametrized_ball_mutation(radius),
p_m=p_m,
pressure=pressure)
islands = []
ga1.initialize()
islands.append(ga1)
best_solution = ga1.best_solution
for iteration in range(n_gen):
ga1.search(iteration, False, False)
for i in range(len(islands)):
if best_solution.fitness < islands[i].best_solution.fitness:
algorithm = islands[i]
best_solution = islands[i].best_solution
print("\n")
fitness_function=ann_i.stimulate,
minimization=False,
validation_threshold=validation_threshold)
#++++++++++++++++++++++++++
# THE SEARCH
# restrictions:
# - 5000 offsprings/run max*
# - 50 offsprings/generation max*
# - use at least 5 runs for your benchmarks
# * including reproduction
#++++++++++++++++++++++++++
ga1 = GeneticAlgorithm(ann_op_i, random_state, ps,
uls.parametrized_tournament_selection(pressure),
uls.one_point_crossover, p_c,
uls.parametrized_ball_mutation(radius), 0.2)
ga2 = GeneticAlgorithm(ann_op_i, random_state, ps,
uls.parametrized_tournament_selection(pressure),
uls.one_point_crossover, p_c,
uls.parametrized_ball_mutation(radius), 0.4)
sa1 = SimulatedAnnealing(ann_op_i, random_state, ps,
uls.parametrized_ball_mutation(radius), control,
update_rate)
search_algorithms = [ga1, ga2, sa1]
# initialize search algorithms
[algorithm.initialize() for algorithm in search_algorithms]
# execute search
[
algorithm.search(n_iterations=n_gen, report=False)