Genetic Algorithms with python 学习笔记ch3

Sorted Numbers

我们需要产生一定长度的有序数列，利用GA来求解。

1. engine

其中 genetic.py 的完整代码为：

import random
import statistics
import sys
import time


def _generate_parent(length, geneSet, get_fitness):
    genes = []
    while len(genes) < length:
        sampleSize = min(length - len(genes), len(geneSet))
        genes.extend(random.sample(geneSet, sampleSize))
    fitness = get_fitness(genes)
    return Chromosome(genes, fitness)


def _mutate(parent, geneSet, get_fitness):
    childGenes = parent.Genes[:]
    index = random.randrange(0, len(parent.Genes))
    newGene, alternate = random.sample(geneSet, 2)
    childGenes[index] = alternate if newGene == childGenes[index] else newGene
    fitness = get_fitness(childGenes)
    return Chromosome(childGenes, fitness)


def get_best(get_fitness, targetLen, optimalFitness, geneSet, display):
    random.seed()
    bestParent = _generate_parent(targetLen, geneSet, get_fitness)
    display(bestParent)
    if bestParent.Fitness >= optimalFitness:
        return bestParent
    while True:
        child = _mutate(bestParent, geneSet, get_fitness)
        if bestParent.Fitness >= child.Fitness:
            continue
        display(child)
        if child.Fitness >= optimalFitness:
            return child
        bestParent = child


class Chromosome:
    def __init__(self, genes, fitness):
        self.Genes = genes
        self.Fitness = fitness


class Benchmark:
    @staticmethod
    def run(function):
        timings = []
        stdout = sys.stdout
        for i in range(100):
            sys.stdout = None
            startTime = time.time()
            function()
            seconds = time.time() - startTime
            sys.stdout = stdout
            timings.append(seconds)
            mean = statistics.mean(timings)
            if i < 10 or i % 10 == 9:
                print("{} {:3.2f} {:3.2f}".format(
                    1 + i, mean,
                    statistics.stdev(timings, mean) if i > 1 else 0))

下面对 genetic.py 中的各个部分进行叙述：

def _generate_parent(length, geneSet, get_fitness):
    genes = []
    while len(genes) < length:
        sampleSize = min(length - len(genes), len(geneSet))
        genes.extend(random.sample(geneSet, sampleSize))
    fitness = get_fitness(genes)
    return Chromosome(genes, fitness)

函数 _generate_parent 的功能是产生父代染色体，这里用于在给定范围geneSet内产生长度为length的数列，数列中的数是随机选取的。

def _mutate(parent, geneSet, get_fitness):
    childGenes = parent.Genes[:]
    index = random.randrange(0, len(parent.Genes))
    newGene, alternate = random.sample(geneSet, 2)
    childGenes[index] = alternate if newGene == childGenes[index] else newGene
    fitness = get_fitness(childGenes)
    return Chromosome(childGenes, fitness)

函数 _mutate 的功能是对给定的parent染色体进行突变，并计算新的适应值，以产生新的染色体。

def get_best(get_fitness, targetLen, optimalFitness, geneSet, display):
    random.seed()
    bestParent = _generate_parent(targetLen, geneSet, get_fitness)
    display(bestParent)
    if bestParent.Fitness >= optimalFitness:
        return bestParent
    while True:
        child = _mutate(bestParent, geneSet, get_fitness)
        if bestParent.Fitness >= child.Fitness:
            continue
        display(child)
        if child.Fitness >= optimalFitness:
            return child
        bestParent = child

函数 get_best 的功能是调用函数 _generate_parent 产生初代染色体，若初代染色体为最优则返回初代染色体；否则进入一个循环，循环内包括：对父代染色体突变，去适应值更优的，在重复循环直到产生最优解。

class Benchmark:
    @staticmethod
    def run(function):
        timings = []
        stdout = sys.stdout
        for i in range(100):
            sys.stdout = None
            startTime = time.time()
            function()
            seconds = time.time() - startTime
            sys.stdout = stdout
            timings.append(seconds)
            mean = statistics.mean(timings)
            if i < 10 or i % 10 == 9:
                print("{} {:3.2f} {:3.2f}".format(
                    1 + i, mean,
                    statistics.stdev(timings, mean) if i > 1 else 0))

类 Benchmark 主要负责对函数function执行100次，输出到目前为止的单次执行的均值和方差。为了减少输出，这里只输出了前十次和后面的每个整十次。

2. sortedNumbersTests.py

首先 sortedNumbersTests.py 的完整代码如下：

import unittest
import datetime
import genetic

class Fitness:
    def __init__(self, numbersInSequenceCount, totalGap):
        self.NumbersInSequenceCount = numbersInSequenceCount
        self.TotalGap = totalGap

    def __gt__(self, other):
        if self.NumbersInSequenceCount != other.NumbersInSequenceCount:
            return self.NumbersInSequenceCount > other.NumbersInSequenceCount
        return self.TotalGap < other.TotalGap

    def __str__(self):
        return "{} Sequential, {} Total Gap".format(
            self.NumbersInSequenceCount,
            self.TotalGap)

def get_fitness(genes):
    fitness = 1
    gap = 0
    for i in range(1, len(genes)):
        if genes[i] > genes[i - 1]:
            fitness += 1
        else:
            gap += genes[i-1] -genes[i]
    return Fitness(fitness, gap)


def display(candidate, startTime):
    timeDiff = datetime.datetime.now() - startTime
    print("{}\t=> {}\t{}".format(
        ', '.join(map(str, candidate.Genes)),
        candidate.Fitness,
        timeDiff
    ))

class SortedNumbersTests(unittest.TestCase):
    def test_sort_10_numbers(self):
        self.sort_numbers(10)

    def test_benchmark(self):
        genetic.Benchmark.run(lambda :self.sort_numbers(40))

    def sort_numbers(self, totalNumbers):
        geneset = [i for i in range(100)]
        startTime = datetime.datetime.now()

        def fnDisplay(candidate):
            display(candidate, startTime)

        def fnGetFitness(genes):
            return get_fitness(genes)

        optimalFitness = Fitness(totalNumbers, 0)
        best = genetic.get_best(fnGetFitness, totalNumbers,
                                optimalFitness, geneset, fnDisplay)
        self.assertTrue(not optimalFitness > best.Fitness)

现在对上面代码的各个部分进行讲解。

class Fitness:
    def __init__(self, numbersInSequenceCount, totalGap):
        self.NumbersInSequenceCount = numbersInSequenceCount
        self.TotalGap = totalGap

    def __gt__(self, other):
        if self.NumbersInSequenceCount != other.NumbersInSequenceCount:
            return self.NumbersInSequenceCount > other.NumbersInSequenceCount
        return self.TotalGap < other.TotalGap

    def __str__(self):
        return "{} Sequential, {} Total Gap".format(
            self.NumbersInSequenceCount,
            self.TotalGap)

首先，声明一个适应值的类，类中包含升序数，和逆序差两个变量。为什么设置这两个变量？因为本次计算的目标是生成一个升序的数列，那么数列中升序的个数越多越好。

举个栗子：
1,2,3,6,5,4 升序数为3
1,2,3,4,5,6 升序数为6
因此后者更好

那么相同升序数的两个数列是不是真的一样好呢？当然不是。因此就引入了逆序差这样的变量。

举个例子：
1,2,10,5,15,16 逆序差为5
1,2,7,5,15,16 逆序差为2
因此后者更好

__gt__是一个内置的大于比较器的查找的函数名称，他表示当一个染色体A的升序数大于另一个染色体B或者当两个染色体A、B升序值相等时，A的逆序差小于B的逆序差时，染色体A的适应值就大于B。

def get_fitness(genes):
    fitness = 1
    gap = 0
    for i in range(1, len(genes)):
        if genes[i] > genes[i - 1]:
            fitness += 1
        else:
            gap += genes[i-1] -genes[i]
    return Fitness(fitness, gap)

函数get_fitness计算染色体的基因也就是数列的适应值，即升序数和逆序差。

class SortedNumbersTests(unittest.TestCase):
    def test_sort_10_numbers(self):
        self.sort_numbers(10)

    def test_benchmark(self):
        genetic.Benchmark.run(lambda :self.sort_numbers(40))

    def sort_numbers(self, totalNumbers):
        geneset = [i for i in range(100)]
        startTime = datetime.datetime.now()

        def fnDisplay(candidate):
            display(candidate, startTime)

        def fnGetFitness(genes):
            return get_fitness(genes)

        optimalFitness = Fitness(totalNumbers, 0)
        best = genetic.get_best(fnGetFitness, totalNumbers,
                                optimalFitness, geneset, fnDisplay)
        self.assertTrue(not optimalFitness > best.Fitness)

由于这里使用了 unittest.TestCase ,因此会执行其中使用test开头的函数。其中，test_sort_10_numbers函数主要是产生一个长度为10的升序数列。而test_benchmark表示的是产生长度为40的升序数列，共执行100次。
sort_numbers调用遗传算法。