self.population.append((random.uniform(-10, 10), random.uniform(-10, 10)))
时间: 2023-11-25 21:07:25 浏览: 20
这段代码是在使用Python语言编写遗传算法的过程中,初始化种群时随机生成种群中个体的染色体(即个体的基因型)。该代码使用了Python标准库中的random模块中的uniform()函数,该函数会随机生成一个指定范围内的浮点数。
具体来说,这段代码生成了一个包含两个元素的元组,每个元素都是一个在-10到10之间的随机浮点数。这个元组表示种群中的一个个体,其中第一个元素表示个体的第一个基因,第二个元素表示个体的第二个基因。实际上,这段代码生成的是一个包含多个个体的种群,每个个体由一个元组表示。
需要注意的是,该代码只是一个示例,实际上在编写遗传算法时,需要根据具体问题来定义个体的染色体结构和种群大小。同时,也需要根据问题来定义适应度函数,以便对每个个体进行评估和选择。
相关问题
def generate_genes(self): genes = [] for i in range(POPULATION_SIZE): gene = { 'x': random.uniform(0, LAND_WIDTH), 'y': random.uniform(0, LAND_HEIGHT), 'height': random.uniform(MIN_HEIGHT, MAX_HEIGHT) } genes.append(gene) return genes怎么缩进
代码缩进应该是这样的:
```
def generate_genes(self):
genes = []
for i in range(POPULATION_SIZE):
gene = {
'x': random.uniform(0, LAND_WIDTH),
'y': random.uniform(0, LAND_HEIGHT),
'height': random.uniform(MIN_HEIGHT, MAX_HEIGHT)
}
genes.append(gene)
return genes
```
在 Python 中,缩进非常重要,因为它用于表示代码块的开始和结束。在这个例子中,`generate_genes` 函数和 `for` 循环的代码块都需要缩进。同时,字典的定义也需要缩进,并且每行键值对之间也需要缩进。
NSGA-II的算法代码实现
以下是NSGA-II算法的Python实现代码:
```python
import random
import copy
class NSGAII:
def __init__(self, num_variables, objectives, constraints, generations, population_size, crossover_prob, mutation_prob):
self.num_variables = num_variables
self.objectives = objectives
self.constraints = constraints
self.generations = generations
self.population_size = population_size
self.crossover_prob = crossover_prob
self.mutation_prob = mutation_prob
self.population = []
self.offspring_population = []
self.fronts = [[]]
self.crowding_distance = []
self.rank = []
self.max_rank = 0
self.min_distance = []
self.min_value = []
self.max_value = []
self.initialize_population()
def initialize_population(self):
for i in range(self.population_size):
individual = []
for j in range(self.num_variables):
individual.append(random.uniform(self.constraints[j][0], self.constraints[j][1]))
self.population.append(individual)
def evaluate_objectives(self, individual):
return [obj(individual) for obj in self.objectives]
def dominates(self, individual1, individual2):
obj1 = self.evaluate_objectives(individual1)
obj2 = self.evaluate_objectives(individual2)
dominates = False
for i in range(len(obj1)):
if obj1[i] < obj2[i]:
dominates = True
elif obj1[i] > obj2[i]:
return False
return dominates
def fast_non_dominated_sort(self, population):
self.fronts = [[]]
self.rank = [-1 for i in range(len(population))]
self.min_distance = [0 for i in range(len(population))]
self.max_rank = 0
for i in range(len(population)):
self.rank[i] = 0
for j in range(len(population)):
if i != j:
if self.dominates(population[i], population[j]):
self.fronts[i].append(j)
elif self.dominates(population[j], population[i]):
self.rank[i] += 1
if self.rank[i] == 0:
self.fronts[0].append(i)
i = 0
while len(self.fronts[i]) > 0:
next_front = []
for j in range(len(self.fronts[i])):
for k in range(len(self.fronts[i][j])):
self.rank[self.fronts[i][j][k]] -= 1
if self.rank[self.fronts[i][j][k]] == 0:
next_front.append(self.fronts[i][j][k])
i += 1
self.fronts.append(next_front)
self.fronts.pop()
def crowding_distance_assignment(self, front):
self.crowding_distance = [0 for i in range(len(front))]
for m in range(len(self.objectives)):
sorted_front = sorted(front, key=lambda individual: self.evaluate_objectives(individual)[m])
self.min_value[m] = self.evaluate_objectives(sorted_front[0])[m]
self.max_value[m] = self.evaluate_objectives(sorted_front[-1])[m]
for i in range(1, len(sorted_front)-1):
self.crowding_distance[i] += (self.evaluate_objectives(sorted_front[i+1])[m] - self.evaluate_objectives(sorted_front[i-1])[m])/(self.max_value[m] - self.min_value[m])
def crowding_distance_sort(self, front):
sorted_front = sorted(front, key=lambda individual: self.crowding_distance[front.index(individual)], reverse=True)
return sorted_front
def selection(self):
selected_population = []
for i in range(self.population_size):
front = self.fronts[random.randint(0, len(self.fronts)-1)]
if len(front) == 1:
selected_population.append(front[0])
else:
sorted_front = self.crowding_distance_sort(front)
selected_population.append(sorted_front[0])
self.population = [self.population[individual] for individual in selected_population]
def crossover(self, parent1, parent2):
child1 = copy.deepcopy(parent1)
child2 = copy.deepcopy(parent2)
if random.random() < self.crossover_prob:
for i in range(self.num_variables):
if random.random() < 0.5:
child1[i] = parent2[i]
child2[i] = parent1[i]
return child1, child2
def mutation(self, individual):
mutated_individual = copy.deepcopy(individual)
for i in range(self.num_variables):
if random.random() < self.mutation_prob:
mutated_individual[i] = random.uniform(self.constraints[i][0], self.constraints[i][1])
return mutated_individual
def create_offspring_population(self):
self.offspring_population = []
for i in range(self.population_size//2):
parent1 = self.population[random.randint(0, self.population_size-1)]
parent2 = self.population[random.randint(0, self.population_size-1)]
child1, child2 = self.crossover(parent1, parent2)
mutated_child1 = self.mutation(child1)
mutated_child2 = self.mutation(child2)
self.offspring_population.append(mutated_child1)
self.offspring_population.append(mutated_child2)
def replace_population(self):
combined_population = self.population + self.offspring_population
self.fast_non_dominated_sort(combined_population)
new_population = []
i = 0
while len(new_population) + len(self.fronts[i]) <= self.population_size:
for individual in self.fronts[i]:
new_population.append(individual)
i += 1
self.crowding_distance_assignment(self.fronts[i])
sorted_front = self.crowding_distance_sort(self.fronts[i])
for j in range(self.population_size - len(new_population)):
new_population.append(sorted_front[j])
self.population = [combined_population[individual] for individual in new_population]
def optimize(self):
for i in range(self.generations):
self.create_offspring_population()
self.replace_population()
print("Generation:", i+1, "Best individual:", self.population[0], "Objectives:", self.evaluate_objectives(self.population[0]))
```
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