遗传算法matlab代码下载

在Matlab中使用遗传算法编写代码是相对简单的。以下是一个基本的遗传算法的Matlab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
mutationRate = 0.01; % 变异率
crossoverRate = 0.8; % 交叉率
generationCount = 100; % 迭代次数
% 初始化种群
population = round(rand(populationSize, chromosomeLength));

% 迭代优化
for generation = 1:generationCount
    % 计算适应度值
    fitness = calculateFitness(population);
    
    % 选择操作
    selectedPopulation = selection(population, fitness);
    
    % 交叉操作
    offspringPopulation = crossover(selectedPopulation, crossoverRate);
    
    % 变异操作
    mutatedPopulation = mutation(offspringPopulation, mutationRate);
    
    % 更新种群
    population = mutatedPopulation;
    
    % 输出当前最优解
    bestFitness = max(fitness);
    disp(['Generation ', num2str(generation), ', Best Fitness: ', num2str(bestFitness)]);
end

% 适应度函数定义，需要根据具体问题进行修改
function fitness = calculateFitness(population)
    % 这里以最大化函数f(x) = x^2 为例
    fitness = sum(population.^2, 2);
end

% 选择操作，这里使用轮＊＊＊ fitness)
    fitnessSum = sum(fitness);
    fitnessRatio = fitness / fitnessSum;
    cumFitnessRatio = cumsum(fitnessRatio);
    
    selectedPopulation = zeros(size(population));
    for i = 1:size(population, 1)
        randNum = rand;
        selectedIdx = find(cumFitnessRatio >= randNum, 1);
        selectedPopulation(i, :) = population(selectedIdx, :);
    end
end

% 交叉操作，这里使用单点交叉
function offspringPopulation = crossover(selectedPopulation, crossoverRate)
    offspringPopulation = selectedPopulation;
    for i = 1:2:size(selectedPopulation, 1)
        if rand <= crossoverRate
            crossoverPoint = randi(size(selectedPopulation, 2));
            offspringPopulation(i, crossoverPoint:end) = selectedPopulation(i+1, crossoverPoint:end);
            offspringPopulation(i+1, crossoverPoint:end) = selectedPopulation(i, crossoverPoint:end);
        end
    end
end

% 变异操作，