matlab 遗传算法轮盘赌代码

遗传算法是一种模拟自然进化过程的优化算法，而轮盘赌选择是遗传算法中的一种常用选择策略。下面是一个简单的MATLAB遗传算法轮盘赌代码示例：

% 遗传算法参数设置
populationSize = 50; % 种群大小
chromosomeLength = 10; % 染色体长度
maxGeneration = 100; % 最大迭代次数
crossoverRate = 0.8; % 交叉概率
mutationRate = 0.01; % 变异概率

% 初始化种群
population = randi([0, 1], populationSize, chromosomeLength);

% 迭代遗传算法
for generation = 1:maxGeneration
    % 计算适应度值
    fitness = calculateFitness(population);
    
    % 轮盘赌选择
    cumulativeFitness = cumsum(fitness);
    selectedPopulation = zeros(populationSize, chromosomeLength);
    for i = 1:populationSize
        r = rand() * cumulativeFitness(end);
        index = find(cumulativeFitness >= r, 1);
        selectedPopulation(i, :) = population(index, :);
    end
    
    % 交叉操作
    for i = 1:2:populationSize
        if rand() < crossoverRate
            crossoverPoint = randi(chromosomeLength - 1);
            temp = selectedPopulation(i, crossoverPoint+1:end);
            selectedPopulation(i, crossoverPoint+1:end) = selectedPopulation(i+1, crossoverPoint+1:end);
            selectedPopulation(i+1, crossoverPoint+1:end) = temp;
        end
    end
    
    % 变异操作
    for i = 1:populationSize
        for j = 1:chromosomeLength
            if rand() < mutationRate
                selectedPopulation(i, j) = 1 - selectedPopulation(i, j);
            end
        end
    end
    
    % 更新种群
    population = selectedPopulation;
end

% 计算适应度值的函数示例
function fitness = calculateFitness(population)
    % 根据问题具体情况计算适应度值
    % 这里假设适应度值为染色体中1的个数
    fitness = sum(population, 2);
end

这段代码实现了一个简单的遗传算法，其中包括种群初始化、适应度计算、轮盘赌选择、交叉操作和变异操作等步骤。你可以根据具体问题进行相应的修改和扩展。