One Max 问题：使用 Numpy

numpy 版本的一最大遗传算法示例与一最大短示例非常相似。个体类继承自 numpy.ndarray。

import numpy
creator.create("Individual", numpy.ndarray, fitness=creator.FitnessMax)

第一个主要区别是实现了在 继承自 Numpy 教程中提到的复制机制的交叉函数。

def cxTwoPointCopy(ind1, ind2):
    """Execute a two points crossover with copy on the input individuals. The
    copy is required because the slicing in numpy returns a view of the data,
    which leads to a self overwriting in the swap operation. It prevents
    ::

        >>> import numpy
        >>> a = numpy.array((1,2,3,4))
        >>> b = numpy.array((5,6,7,8))
        >>> a[1:3], b[1:3] = b[1:3], a[1:3]
        >>> print(a)
        [1 6 7 4]
        >>> print(b)
        [5 6 7 8]
    """
    size = len(ind1)
    cxpoint1 = random.randint(1, size)
    cxpoint2 = random.randint(1, size - 1)
    if cxpoint2 >= cxpoint1:
        cxpoint2 += 1
    else: # Swap the two cx points
        cxpoint1, cxpoint2 = cxpoint2, cxpoint1

    ind1[cxpoint1:cxpoint2], ind2[cxpoint1:cxpoint2] \
        = ind2[cxpoint1:cxpoint2].copy(), ind1[cxpoint1:cxpoint2].copy()

    return ind1, ind2

此交叉功能被添加到工具箱中，而不是原始的 deap.tools.cxTwoPoint() 交叉功能。

toolbox.register("mate", cxTwoPointCopy)

第二个主要区别是 HallOfFame 中使用了 similar 函数，该函数必须设置为 numpy.array_equal() 或 numpy.allclose()。

    hof = tools.HallOfFame(1, similar=numpy.array_equal)

完整的源代码：examples/ga/onemax_numpy。