matlab遗传算法函数代码

当然，我可以为你提供一个简单的 MATLAB 遗传算法函数代码示例：

function [bestSolution, bestFitness] = geneticAlgorithm(populationSize, chromosomeLength, mutationRate, crossoverRate, numGenerations)
    % 初始化种群
    population = randi([0, 1], populationSize, chromosomeLength);
    
    for generation = 1:numGenerations
        % 计算适应度
        fitness = calculateFitness(population);
        
        % 选择父代
        selectedParents = selection(population, fitness);
        
        % 交叉操作
        offspring = crossover(selectedParents, crossoverRate);
        
        % 变异操作
        mutatedOffspring = mutation(offspring, mutationRate);
        
        % 合并父代和子代种群
        combinedPopulation = [population; mutatedOffspring];
        
        % 选择新的种群
        population = elitismSelection(combinedPopulation, fitness, populationSize);
    end
    
    % 计算最佳个体和适应度
    bestFitness = max(fitness);
    bestSolution = population(find(fitness == bestFitness, 1), :);
end

function fitness = calculateFitness(population)
    % 计算适应度，根据具体问题进行定义
    fitness = sum(population, 2);
end

function selectedParents = selection(population, fitness)
    % 轮盘赌选择方法
    cumulativeFitness = cumsum(fitness);
    totalFitness = sum(fitness);
    randomNumbers = rand(size(population, 1), 1) * totalFitness;
    
    selectedParents = zeros(size(population));
    
    for i = 1:size(population, 1)
        index = find(cumulativeFitness >= randomNumbers(i), 1);
        selectedParents(i, :) = population(index, :);
    end
end

function offspring = crossover(selectedParents, crossoverRate)
    % 单点交叉操作
    offspring = zeros(size(selectedParents));

    for i = 1:2:size(selectedParents, 1)
        if rand() < crossoverRate
            crossoverPoint = randi([2, size(selectedParents, 2) - 1]);
            offspring(i, :) = [selectedParents(i, 1:crossoverPoint), selectedParents(i + 1, crossoverPoint + 1:end)];
            offspring(i + 1, :) = [selectedParents(i + 1, 1:crossoverPoint), selectedParents(i, crossoverPoint + 1:end)];
        else
            offspring(i, :) = selectedParents(i, :);
            offspring(i + 1, :) = selectedParents(i + 1, :);
        end
    end
end

function mutatedOffspring = mutation(offspring, mutationRate)
    % 每个基因位以一定概率进行变异
    mutatedOffspring = offspring;
    
    for i = 1:size(offspring, 1)
        for j = 1:size(offspring, 2)
            if rand() < mutationRate
                mutatedOffspring(i, j) = ~mutatedOffspring(i, j);
            end
        end
    end
end

function newPopulation = elitismSelection(combinedPopulation, fitness, populationSize)
    % 精英选择，保留适应度最高的个体进入下一代种群
    [~, sortedIndices] = sort(fitness, 'descend');
    newPopulation = combinedPopulation(sortedIndices(1:populationSize), :);
end

这是一个简单的遗传算法函数代码，包含了种群初始化、适应度计算、选择、交叉、变异和精英选择等基本步骤。你可以根据具体问题对代码进行相应的修改和扩展。希望能对你有所帮助！