遗传算法matlab实例

当然没问题！下面是一个简单的遗传算法的 MATLAB 实例：

% 遗传算法参数设置
popSize = 50;              % 种群大小
numGenes = 10;             % 染色体长度
mutationRate = 0.01;       % 变异率
tournamentSize = 5;        % 锦标赛选择的参与者数量
maxGenerations = 100;      % 最大迭代次数

% 初始化种群
population = round(rand(popSize, numGenes));

% 迭代优化
for generation = 1:maxGenerations
    % 计算适应度
    fitness = sum(population, 2);
    
    % 选择父代
    parents = tournamentSelection(population, fitness, tournamentSize);
    
    % 生成子代
    offspring = crossover(parents);
    
    % 变异
    offspring = mutation(offspring, mutationRate);
    
    % 更新种群
    population = offspring;
    
    % 显示当前最优解
    [~, idx] = max(fitness);
    bestIndividual = population(idx, :);
    disp(['Generation ', num2str(generation), ': Best Individual = ', num2str(bestIndividual)]);
end

% 锦标赛选择函数
function selectedParents = tournamentSelection(population, fitness, tournamentSize)
    numParents = size(population, 1);
    selectedParents = zeros(numParents, size(population, 2));
    
    for i = 1:numParents
        tournamentIndices = randperm(numParents, tournamentSize);
        tournamentFitness = fitness(tournamentIndices);
        [~, idx] = max(tournamentFitness);
        selectedParents(i, :) = population(tournamentIndices(idx), :);
    end
end

% 单点交叉函数
function offspring = crossover(parents)
    numParents = size(parents, 1);
    numGenes = size(parents, 2);
    offspring = zeros(numParents, numGenes);
    
    for i = 1:numParents
        parent1 = parents(i, :);
        parent2 = parents(mod(i, numParents) + 1, :);
        crossoverPoint = randi(numGenes - 1);
        offspring(i, :) = [parent1(1:crossoverPoint), parent2(crossoverPoint+1:end)];
    end
end

% 变异函数
function mutatedOffspring = mutation(offspring, mutationRate)
    numOffspring = size(offspring, 1);