def genetic_probing(data, seed):
random.seed(seed)
optimizer = ga.GeneticOptimizer(TSPEvaluator(data),
1000,
max_fitness=BEST_OVERALL)
optimizer.reset_fitness(-1e99) # required for searching for shortest path
optimizer.max_stagnation = 300
optimizer.selection = ga.RankBasedSelection()
optimizer.mate = ga.MateOrderedCX()
optimizer.crossover_rate = 0.9
optimizer.mutation = ga.ScrambleMutate(5)
optimizer.mutation_rate = 0.1
# count >= elitism, stores the <count> overall best solutions
optimizer.hall_of_fame.count = ELITISM
# preserve <elitism> overall best solutions in each generation
optimizer.elitism = ELITISM
optimizer.add_candidates(ga.UniqueIntDNA.n_random(300, length=len(data)))
optimizer.execute(feedback, 2)
print(
f"GeneticOptimizer: {optimizer.generation} generations x {optimizer.count} "
f"DNA strands, best result:")
evaluator = cast(TSPEvaluator, optimizer.evaluator)
best_dist = abs(evaluator.evaluate(optimizer.best_dna))
percent = best_dist / abs(BEST_OVERALL) * 100
print(f"Shortest path overall: {abs(BEST_OVERALL):.3f}")
print(
f"Shortest path found (seed={seed}): {best_dist:.3f} ({percent:.1f}%)")
print(optimizer.best_dna)
name = f"bay29, dist={int(best_dist)} ({percent:.1f}%), seed={seed}"
show_log(optimizer.log, name)
show_result(data, optimizer.best_dna, name)
def make_subset_optimizer(packer: bp.AbstractPacker, generations: int,
dna_count: int):
evaluator = bp.SubSetEvaluator(packer)
optimizer = ga.GeneticOptimizer(evaluator, max_generations=generations)
optimizer.name = "pack item subset"
optimizer.add_candidates(ga.BitDNA.n_random(dna_count, len(packer.items)))
return optimizer
def test_can_only_run_once(self, packer):
evaluator = DummyEvaluator(packer)
optimizer = ga.GeneticOptimizer(evaluator, 100)
optimizer.execute()
assert optimizer.is_executed is True
with pytest.raises(TypeError):
optimizer.execute()
def test_execution(self, packer):
packer.add_bin(*MEDIUM_BOX)
evaluator = DummyEvaluator(packer)
optimizer = ga.GeneticOptimizer(evaluator, 10)
optimizer.add_candidates(ga.BitDNA.n_random(20, len(packer.items)))
optimizer.execute()
assert optimizer.generation == 10
assert optimizer.best_fitness > 0.1
# Get best packer of SubSetEvaluator:
best_packer = evaluator.run_packer(optimizer.best_dna)
assert len(best_packer.bins[0].items) > 1
def run_optimizer(packer):
def feedback(optimizer: ga.GeneticOptimizer):
print(f"gen: {optimizer.generation:4}, "
f"stag: {optimizer.stagnation:4}, "
f"fitness: {optimizer.best_fitness:.3f}")
return False
evaluator = bp.SubSetEvaluator(packer)
optimizer = ga.GeneticOptimizer(evaluator, max_generations=GENERATIONS)
optimizer.name = "pack item subset"
optimizer.crossover_rate = 0.9
optimizer.mutation_rate = 0.01
optimizer.add_candidates(ga.BitDNA.n_random(DNA_COUNT, len(packer.items)))
print(
f"\nGenetic algorithm search: {optimizer.name}\n"
f"max generations={optimizer.max_generations}, DNA count={optimizer.count}"
)
optimizer.execute(feedback, interval=3)
print(
f"GeneticOptimizer: {optimizer.generation} generations x {optimizer.count} "
f"candidates, best result:")
evaluator = cast(bp.SubSetEvaluator, optimizer.evaluator)
best_packer = evaluator.run_packer(optimizer.best_dna)
return best_packer
def test_init_invalid_max_runs(self, packer):
with pytest.raises(ValueError):
ga.GeneticOptimizer(packer, 0)
def test_init(self, packer):
driver = ga.GeneticOptimizer(packer, 100)
assert driver.is_executed is False