遗传算法代码matlab

以下是一个简单的遗传算法的 MATLAB 代码示例：

% 遗传算法参数
popSize = 50; % 种群大小
chromLength = 20; % 染色体长度
mutationRate = 0.01; % 变异率
crossoverRate = 0.8; % 交叉率
maxGenerations = 100; % 最大迭代次数

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

% 迭代遗传算法
for generation = 1:maxGenerations
    % 计算适应度
    fitness = calculateFitness(population);
    
    % 选择
    selectedPopulation = selection(population, fitness);
    
    % 交叉
    offspringPopulation = crossover(selectedPopulation, crossoverRate);
    
    % 变异
    mutatedPopulation = mutation(offspringPopulation, mutationRate);
    
    % 更新种群
    population = mutatedPopulation;
    
    % 显示每一代的最佳适应度
    bestFitness = max(fitness);
    disp(['Generation: ', num2str(generation), ', Best Fitness: ', num2str(bestFitness)]);
end

% 计算适应度函数的示例（需要根据具体问题进行定义）
function fitness = calculateFitness(population)
    % 这里是一个示例，计算染色体中所有基因的和作为适应度函数
    fitness = sum(population, 2);
end

% 选择操作的示例（使用轮盘赌选择法）
function selectedPopulation = selection(population, fitness)
    selectedPopulation = zeros(size(population));
    
    % 计算适应度总和
    totalFitness = sum(fitness);
    
    % 计算每个个体被选择的概率
    selectionProbabilities = fitness ./ totalFitness;
    
    % 执行选择操作
    for i = 1:size(population, 1)
        selectedIdx = find(rand <= cumsum(selectionProbabilities), 1);
        selectedPopulation(i, :) = population(selectedIdx, :);
    end
end

% 交叉操作的示例（单点交叉）
function offspringPopulation = crossover(selectedPopulation, crossoverRate)
    offspringPopulation = zeros(size(selectedPopulation));
    
    for i = 1:2:size(selectedPopulation, 1)
        % 随机选择交叉点
        if rand <= crossoverRate
            crossoverPoint = randi([2, size(selectedPopulation, 2) - 1]);
            
            % 执行交叉操作
            offspringPopulation(i, :) = [selectedPopulation(i, 1:crossoverPoint), selectedPopulation(i+1, crossoverPoint+1:end)];
            offspringPopulation(i+1, :) = [selectedPopulation(i+1, 1:crossoverPoint), selectedPopulation(i, crossoverPoint+1:end)];
        else
            % 若不进行交叉，则直接复制父代个体到子代
            offspringPopulation(i, :) = selectedPopulation(i, :);
            offspringPopulation(i+1, :) = selectedPopulation(i+1, :);
        end
    end
end

% 变异操作的示例（位翻转变异）
function mutatedPopulation = mutation(offspringPopulation, mutationRate)
    mutatedPopulation = offspringPopulation;
    
    % 遍历所有个体中的所有基因
    for i = 1:size(mutatedPopulation, 1)
        for j = 1:size(mutatedPopulation, 2)
            % 根据变异率进行翻转操作
            if rand <= mutationRate
                mutatedPopulation(i, j) = ~mutatedPopulation(i, j);
            end
        end
    end
end

这个代码示例是一个基本的遗传算法框架，你可以根据具体的问题进行适应度函数的定义和其他参数的调整。请记住，这只是一个简单的示例，你可能需要根据自己的问题进行一些修改和优化。