我需要一段MATLAB的模因算法代码，其中适应度函数为单机调度中目标函数为加权总延迟最小，交叉操作使用PMX，不进行变异操作，局部搜索使用模拟退火算法

以下是一段MATLAB的模因算法代码，其中适应度函数为单机调度中目标函数为加权总延迟最小，交叉操作使用PMX，不进行变异操作，局部搜索使用模拟退火算法：

% 定义适应度函数
function fitness = calculateFitness(schedule)
    % 计算加权总延迟
    totalDelay = 0;
    for i = 1:length(schedule)
        totalDelay = totalDelay + schedule(i).weight * max(0, schedule(i).finish - schedule(i).due);
    end
    % 适应度为加权总延迟的相反数
    fitness = -totalDelay;
end

% 定义交叉操作
function offspring = crossover(parent1, parent2)
    % 使用PMX交叉操作
    n = length(parent1);
    cut1 = randi(n-1);
    cut2 = randi([cut1+1, n]);
    offspring = parent1;
    mapping = zeros(1, n);
    mapping(cut1:cut2) = parent2(cut1:cut2);
    for i = cut1:cut2
        if offspring(i) == mapping(i)
            continue;
        end
        idx = find(mapping == offspring(i));
        while ~isempty(idx) && (idx < cut1 || idx > cut2)
            offspring(i) = mapping(idx);
            idx = find(mapping == offspring(i));
        end
    end
end

% 定义局部搜索操作
function newSchedule = localSearch(schedule)
    % 使用模拟退火算法进行局部搜索
    T = 100;
    alpha = 0.9;
    k = 1;
    currentSchedule = schedule;
    currentFitness = calculateFitness(currentSchedule);
    while T > 1e-3
        newSchedule = currentSchedule;
        % 随机交换两个任务的顺序
        idx1 = randi(length(newSchedule));
        idx2 = randi(length(newSchedule));
        temp = newSchedule(idx1);
        newSchedule(idx1) = newSchedule(idx2);
        newSchedule(idx2) = temp;
        newFitness = calculateFitness(newSchedule);
        delta = newFitness - currentFitness;
        if delta < 0 || exp(-delta/T) > rand()
            currentSchedule = newSchedule;
            currentFitness = newFitness;
        end
        T = alpha * T;
        k = k + 1;
    end
    newSchedule = currentSchedule;
end

% 定义模因算法
function [bestSchedule, bestFitness] = memeticAlgorithm(schedule)
    % 设置参数
    popSize = 50;
    maxGenerations = 100;
    eliteCount = 5;
    % 初始化种群
    population = repmat(schedule, popSize, 1);
    for i = 1:popSize
        population(i).order = randperm(length(schedule));
    end
    % 进化过程
    for generation = 1:maxGenerations
        % 计算适应度
        fitness = arrayfun(@calculateFitness, population);
        % 选择精英
        [~, eliteIdx] = sort(fitness, 'descend');
        elite = population(eliteIdx(1:eliteCount));
        % 进行交叉操作
        offspring = repmat(schedule, popSize-eliteCount, 1);
        for i = 1:popSize-eliteCount
            parent1 = elite(randi(eliteCount));
            parent2 = elite(randi(eliteCount));
            offspring(i) = crossover(parent1, parent2);
        end
        % 合并种群
        population = [elite; offspring];
        % 进行局部搜索操作
        for i = 1:popSize
            population(i) = localSearch(population(i));
        end
    end
    % 返回最优解和最优适应度
    fitness = arrayfun(@calculateFitness, population);
    [~, bestIdx] = max(fitness);
    bestSchedule = population(bestIdx);
    bestFitness = fitness(bestIdx);
end

注意：此代码仅供参考，实际应用中可能需要根据具体问题进行修改和调整。