Python 2.7 使用DEAP编写我的第一个遗传算法-遇到类型错误

我刚开始使用DEAP的第一个GA算法。作为一个初学者，我只是尝试去激活一个我已经用python编写的算法。 当我试图通过在工具箱容器中注册来评估我的适应度函数时，我得到以下错误

!！[错误列表][1]

    Traceback (most recent call last):
    fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
    File "number_optimize_DEAP.py", line 38, in grade
        summed = reduce(add, (fitness(x) for x in population),0)
    File "number_optimize_DEAP.py", line 38, in <genexpr>
        summed = reduce(add, (fitness(x) for x in population),0)
    File "number_optimize_DEAP.py", line 29, in fitness
        sum = reduce(add,individual,0)
    TypeError: reduce() arg 2 must support iteration'

“计算平均人口适应度”

def grade(population):

    summed = reduce(add, (fitness(x) for x in population),0)
    return summed / (len(population) * 1.0)

toolbox.register("evaluate",grade)
toolbox.register("Crossover",tools.cxOnePoint)
toolbox.register("Mutate",tools.mutUniformInt,indpb = 0.01)
toolbox.register("Selection",tools.selBest)

def evolution():
    pop = toolbox.Population(n = 100)
    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean)
    stats.register("min",numpy.min)
    stats.register("max", numpy.max)

pop,logbook = algorithms.eaSimple(pop,toolbox, cxpb = 0.2, mutpb = 0.01, ngen = 10, stats         = stats, halloffame = hof, verbose = True)

'适应度函数计算个体中元素的总和，并查看其与目标的距离'

“等级计算平均适合度”

---

请帮我摆脱困境！！提前感谢

错误消息很清楚：fitness正在尝试使用reduce对个人进行迭代，但无法对任何个人进行迭代。@jonrsharpe感谢您的帮助。但“个人”在这里是一个列表，我如何解决“不能迭代的问题”？你确定吗？如果您之前将print typeindividual放在行上会发生什么？您是如何定义toolbox.Population的？您能在创建pop的地方添加代码段吗？另外，reduce函数不是内置的。它有什么作用？

def grade(population):

    summed = reduce(add, (fitness(x) for x in population),0)
    return summed / (len(population) * 1.0)

toolbox.register("evaluate",grade)
toolbox.register("Crossover",tools.cxOnePoint)
toolbox.register("Mutate",tools.mutUniformInt,indpb = 0.01)
toolbox.register("Selection",tools.selBest)

def evolution():
    pop = toolbox.Population(n = 100)
    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean)
    stats.register("min",numpy.min)
    stats.register("max", numpy.max)

pop,logbook = algorithms.eaSimple(pop,toolbox, cxpb = 0.2, mutpb = 0.01, ngen = 10, stats         = stats, halloffame = hof, verbose = True)