用python写一个GAF
时间: 2024-05-05 21:15:53 浏览: 15
抱歉,我是一名AI语言模型,无法提供图形用户界面(GUI)的编程。但是,下面是一个简单的Python GAF模板:
```python
import random
# GAF参数
POPULATION_SIZE = 10
GENERATION_COUNT = 100
CROSSOVER_RATE = 0.8
MUTATION_RATE = 0.05
# 基因序列长度
GENE_LENGTH = 10
# 目标基因序列
TARGET_GENE = [1, 0, 1, 1, 0, 1, 0, 0, 1, 0]
# 随机生成初始种群
def generate_population(size):
population = []
for i in range(size):
chromosome = []
for j in range(GENE_LENGTH):
gene = random.randint(0, 1)
chromosome.append(gene)
population.append(chromosome)
return population
# 计算适应度
def calculate_fitness(chromosome):
fitness = 0
for i in range(GENE_LENGTH):
if chromosome[i] == TARGET_GENE[i]:
fitness += 1
return fitness
# 选择操作
def selection(population):
fitness_scores = []
for chromosome in population:
fitness_scores.append(calculate_fitness(chromosome))
total_fitness = sum(fitness_scores)
roulette_wheel = []
for i in range(len(population)):
slice_size = int(fitness_scores[i] / total_fitness * 100)
for j in range(slice_size):
roulette_wheel.append(i)
selected = []
while len(selected) < len(population):
index = random.choice(roulette_wheel)
selected.append(population[index])
return selected
# 交叉操作
def crossover(population):
for i in range(len(population) - 1):
if random.random() < CROSSOVER_RATE:
crossover_point = random.randint(1, GENE_LENGTH - 1)
parent1 = population[i]
parent2 = population[i + 1]
child1 = parent1[:crossover_point] + parent2[crossover_point:]
child2 = parent2[:crossover_point] + parent1[crossover_point:]
population[i] = child1
population[i + 1] = child2
return population
# 变异操作
def mutation(population):
for i in range(len(population)):
for j in range(GENE_LENGTH):
if random.random() < MUTATION_RATE:
gene = random.randint(0, 1)
population[i][j] = gene
return population
# 运行GAF
def run_gaf():
population = generate_population(POPULATION_SIZE)
for i in range(GENERATION_COUNT):
population = selection(population)
population = crossover(population)
population = mutation(population)
print("Generation:", i + 1, "Best fitness:", max([calculate_fitness(chromosome) for chromosome in population]))
if max([calculate_fitness(chromosome) for chromosome in population]) == GENE_LENGTH:
break
if __name__ == '__main__':
run_gaf()
```
该模板实现了一个简单的GAF,用于寻找一个基因序列,使其与目标基因序列匹配。该模板包括以下功能:
- 随机生成初始种群
- 计算适应度
- 选择操作
- 交叉操作
- 变异操作
您可以根据需要修改参数和实现更复杂的功能。
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