遗传基因算法实现 OLED mask layout 代码案例
时间: 2023-08-06 14:21:31 浏览: 144
以下是一个简单的Python代码示例,演示如何使用遗传算法实现OLED mask layout。这里使用了DEAP库来实现遗传算法的各个组件。代码仅供参考,具体实现需要根据具体问题进行调整。
```python
import random
from deap import base, creator, tools
# 定义问题和适应度函数
def eval_mask_layout(individual):
# 计算布局的适应度
return fitness_value,
# 定义遗传算法的参数
POPULATION_SIZE = 100
CROSSOVER_PROBABILITY = 0.5
MUTATION_PROBABILITY = 0.2
NUM_GENERATIONS = 50
# 创建遗传算法的工具箱
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", eval_mask_layout)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)
# 初始化种群
population = toolbox.population(n=POPULATION_SIZE)
# 迭代遗传算法
for gen in range(NUM_GENERATIONS):
# 评估适应度
fitnesses = list(map(toolbox.evaluate, population))
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
# 选择下一代
offspring = toolbox.select(population, len(population))
# 复制精英个体
offspring = list(map(toolbox.clone, offspring))
# 交叉操作
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() < CROSSOVER_PROBABILITY:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
# 变异操作
for mutant in offspring:
if random.random() < MUTATION_PROBABILITY:
toolbox.mutate(mutant)
del mutant.fitness.values
# 评估新一代适应度
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# 更新种群
population[:] = offspring
# 打印最优解
best_ind = tools.selBest(population, k=1)[0]
print("Best individual is ", best_ind)
print("Fitness value is ", best_ind.fitness.values[0])
```
上述代码中,`eval_mask_layout`函数表示了适应度函数,`creator`模块用于定义个体和适应度函数,`toolbox`模块用于注册遗传算法的各个组件。在主循环中,首先对种群进行评估,然后进行选择、交叉、变异等操作,最后更新种群。最优解可以通过`tools.selBest`函数获取。