def selection(self, fitness_values: List[float]) -> List[int]:
probabilities = listtools.normListSumTo(fitness_values, 1)
parent_indices = []
random.seed()
parent1_probability = random.random()
parent2_probability = random.random()
summ = 0
for i in range(self.config['population_size']):
if len(parent_indices) == 2:
break
next_sum = summ + probabilities[i]
if next_sum >= parent1_probability >= summ:
parent_indices.append(i)
if next_sum >= parent2_probability >= summ:
parent_indices.append(i)
summ = next_sum
return parent_indices
def selection(fitness_values): # normalize the list so we have probabilities to pick parents fitness_values = listtools.normListSumTo(fitness_values, 1) parents = [] random.seed() parent1_probability = random.random() parent2_probability = random.random() sum = 0 for i in range(POPULATION_SIZE): if len(parents) == 2: break next_sum = sum + fitness_values[i] if parent1_probability <= next_sum and parent1_probability >= sum: parents.append(i) if parent2_probability <= next_sum and parent2_probability >= sum: parents.append(i) sum = next_sum return parents
def selection(self, fitness_values):
"""
Pick two parents according to probability represented by normalized fitness values
3a[Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected)
"""
# normalize the list so we have probabilities to pick parents
fitness_values = listtools.normListSumTo(fitness_values, 1)
parentIndices = []
random.seed()
parent1_probability = random.random()
parent2_probability = random.random()
sum = 0
for i in range(self.config['population_size']):
if len(parentIndices) == 2:
break
next_sum = sum + fitness_values[i]
if parent1_probability <= next_sum and parent1_probability >= sum:
parentIndices.append(i)
if parent2_probability <= next_sum and parent2_probability >= sum:
parentIndices.append(i)
sum = next_sum
return parentIndices
def selection(self, fitness_values):
"""
Pick two parents according to probability represented by normalized fitness values
3a[Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected)
"""
# normalize the list so we have probabilities to pick parents
fitness_values = listtools.normListSumTo(fitness_values, 1)
parentIndices = []
random.seed()
parent1_probability = random.random()
parent2_probability = random.random()
sum = 0
for i in range(self.config['population_size']):
if len(parentIndices) == 2:
break
next_sum = sum + fitness_values[i]
if parent1_probability <= next_sum and parent1_probability >= sum:
parentIndices.append(i)
if parent2_probability <= next_sum and parent2_probability >= sum:
parentIndices.append(i)
sum = next_sum
return parentIndices
def test_normListSumTo(self):
lista = [1, 2, 5, 2]
list2 = listtools.normListSumTo(lista, 1)
self.assertEqual(sum(list2), 1)
self.assertListEqual(list2, [0.1, 0.2, 0.5, 0.2])