遗传算法的matlab应用
时间: 2023-12-22 21:28:39 浏览: 23
```matlab
% 基本遗传算法MATLAB程序示例
% 1. 初始化种群
populationSize = 50; % 种群大小
chromosomeLength = 10; % 染色体长度
population = round(rand(populationSize, chromosomeLength)); % 随机产生初始群体
% 2. 适应度函数评价
fitness = sum(population, 2); % 以染色体中1的个数作为适应度值
% 3. 选择
selectionProbability = fitness / sum(fitness); % 计算选择概率
cumulativeProbability = cumsum(selectionProbability); % 计算累积概率
selectedPopulation = population(find(cumulativeProbability > rand));
% 4. 交叉
crossoverRate = 0.8; % 交叉概率
crossoverPopulation = zeros(populationSize, chromosomeLength);
for i = 1:2:populationSize
if rand < crossoverRate
crossoverPoint = randi([1, chromosomeLength - 1]);
crossoverPopulation(i, :) = [selectedPopulation(i, 1:crossoverPoint), selectedPopulation(i+1, crossoverPoint+1:end)];
crossoverPopulation(i+1, :) = [selectedPopulation(i+1, 1:crossoverPoint), selectedPopulation(i, crossoverPoint+1:end)];
else
crossoverPopulation(i, :) = selectedPopulation(i, :);
crossoverPopulation(i+1, :) = selectedPopulation(i+1, :);
end
end
% 5. 变异
mutationRate = 0.01; % 变异概率
mutationPopulation = crossoverPopulation;
for i = 1:populationSize
for j = 1:chromosomeLength
if rand < mutationRate
mutationPopulation(i, j) = ~mutationPopulation(i, j);
end
end
end
% 6. 更新种群
newPopulation = mutationPopulation;
```
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