问题描述
我正在编写用于实现遗传算法的 Python 代码。
我一直在创造孩子。我的研究需要这个。我已尽我所能实现代码。
def initialise_city(num_dim,limit = 100):
X = np.random.randint(0,limit,size=num_dim)
return X
def initialise_cities(num_cities):
cities = []
for i in range(num_cities):
cities.append(initialise_city(2))
return cities
num_cities = 5
cities = initialise_cities(num_cities)
print("City Positions: ",cities)
def distance_function(cities,visit_order):
distance = 0.0
visit_pos = 0
next_pos = 0
for i,txt in enumerate(visit_order):
if (i < len(visit_order)-1):
visit_pos = visit_order[i]
next_pos = visit_order[i+1]
distance = distance + np.sqrt((cities[visit_pos][0]-cities[next_pos][0])**2 +(cities[visit_pos][1]-cities[next_pos][1])**2)
#raise NotImplementedError()
return -1.0*distance
def initialise_chromosome(chromosome_size):
# YOUR CODE HERE
chromosome = np.random.permutation(chromosome_size)
#raise NotImplementedError()
return chromosome
def initialise_population(population_size,chromosome_size):
population = []
# YOUR CODE HERE
for i in range(population_size):
population.append(initialise_chromosome(chromosome_size))
#raise NotImplementedError()
return population
def calculate_fitness(population,cities,fitness_function):
fitness_list = []
# YOUR CODE HERE
d = 0.0
for i,ix in enumerate(population):
d = fitness_function(cities,ix)
fitness_list.append(fitness_function(cities,ix))
#raise NotImplementedError()
return fitness_list
def selection(population,fitness_list):
## Select the top half of the best of the population
population = np.array(population)
sorted_indices = np.argsort(fitness_list)
selection_point = int(1+ len(fitness_list)/2)
# Randomply permute this top half of the poulation
indices = np.random.choice(sorted_indices[:selection_point],len(population))
best_population = population[indices]
return best_population
def pairing(selected_population):
## pair up parents that will be used to reproduce
count = 0
pairs = []
while count < len(selected_population)-1:
index = count
pairs.append([selected_population[index],selected_population[index+1]])
count +=2
return pairs
我被困在应该创建孩子的部分。
def create_child(a,b):
child = []
# YOUR CODE HERE
point = random.randint(1,len(pairs))
#raise NotImplementedError()
return child
def cross_over(pairs):
final_population = []
for a,b in pairs:
child = create_child(a,b)
final_population.append(child)
child = create_child(b,a)
final_population.append(child)
return final_population
解决方法
暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！
如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。
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