Genetic Algorithms with python 学习笔记ch2

One Max Problem

最大化基因中的数字 ’1‘ ，并且genes只能为 0 或 1 。

1.将基因数据类型改为列表并求解

利用原来的引擎进行求解，修改后的 guessPasswordTest.py 代码如下：

import datetime
import random
import unittest

import genetic


def get_fitness(guess, target):
    return sum(1 for expected, actual in zip(target, guess)
               if expected == actual)


def display(candidate, startTime):
    timeDiff = datetime.datetime.now() - startTime
    print("{}	{}	{}".format(
        ''.join(candidate.Genes), candidate.Fitness, timeDiff))


class GuessPasswordTests(unittest.TestCase):
    geneset = " abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!.,"

    def test_onemax(self):
        target = "1" * 100
        self.geneset = "01"
        self.guess_password(target)

    def guess_password(self, target):
        startTime = datetime.datetime.now()

        def fnGetFitness(genes):
            return get_fitness(genes, target)

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

        optimalFitness = len(target)
        best = genetic.get_best(fnGetFitness, len(target), optimalFitness,
                                self.geneset, fnDisplay)
        self.assertEqual(''.join(best.Genes), target)

    def test_benchmark(self):
        genetic.Benchmark.run(self.test_onemax)


if __name__ == '__main__':
    unittest.main()

同时，genetic.py 内容不变：

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):
    index = random.randrange(0, len(parent.Genes))
    childGenes = 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))

2.建立OneMax test class

oneMaxTest.py的内容为：

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):
    index = random.randrange(0, len(parent.Genes))
    childGenes = 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))