Genetic Algorithm遗传算法，两个代码实现例子

通过了解遗传算法的概念，应用和代码实现，来充分理解和学习遗传算法。本文文末包含两个不同的通俗易懂的例子，分别使用java和python实现。

什么是遗传算法？

可参考这篇文章：link
应用：List of genetic algorithm applications
（1）工程设计
（2）旅行商问题

（3）机器人

（4）著名的组合优化问题「背包问题」
比如，你准备要去野游 1 个月，但是你只能背一个限重 30 公斤的背包。现在你有不同的必需物品，它们每一个都有自己的「生存点数」（具体在下表中已给出）。因此，你的目标是在有限的背包重量下，最大化你的「生存点数」。The image is from blog

3. （例子1）使用Java实现遗传算法的一个应用。

给定一组5个基因，每个基因可以保持二进制值0和1。

适应度值计算为基因组中存在的1的数量。如果有五个1，那么它具有最大适应性。如果没有1，那么它具有最小适应度。

该遗传算法试图最大化适应度以提供由最适合的个体组成的群体，即具有五个1的个体。

注意：在这个例子中，每次迭代，找到适应度最好的两个个体，他们交叉和变异之后，分别生成两个子个体，然后从这两个子个体中选择适应度最强的一个，来替换掉群体中适应度最差的个体。

当生成一个个体，它的所有的基因都为1时，程序结束。


import java.util.Random;/**** @author Vijini*///Main class
public class SimpleDemoGA {Population population = new Population();Individual fittest;Individual secondFittest;int generationCount = 0;public static void main(String[] args) {Random rn = new Random();SimpleDemoGA demo = new SimpleDemoGA();//Initialize populationdemo.population.initializePopulation(10);//Calculate fitness of each individualdemo.population.calculateFitness();System.out.println("Generation: " + demo.generationCount + " Fittest: " + demo.population.fittest);//While population gets an individual with maximum fitnesswhile (demo.population.fittest < 5) {++demo.generationCount;//Do selectiondemo.selection();//Do crossoverdemo.crossover();//Do mutation under a random probabilityif (rn.nextInt()%7 < 5) {demo.mutation();}//Add fittest offspring to populationdemo.addFittestOffspring();//Calculate new fitness valuedemo.population.calculateFitness();System.out.println("Generation: " + demo.generationCount + " Fittest: " + demo.population.fittest);}System.out.println("\nSolution found in generation " + demo.generationCount);System.out.println("Fitness: "+demo.population.getFittest().fitness);System.out.print("Genes: ");for (int i = 0; i < 5; i++) {System.out.print(demo.population.getFittest().genes[i]);}System.out.println("");}//Selectionvoid selection() {//Select the most fittest individualfittest = population.getFittest();//Select the second most fittest individualsecondFittest = population.getSecondFittest();}//Crossovervoid crossover() {Random rn = new Random();//Select a random crossover pointint crossOverPoint = rn.nextInt(population.individuals[0].geneLength);//Swap values among parentsfor (int i = 0; i < crossOverPoint; i++) {int temp = fittest.genes[i];fittest.genes[i] = secondFittest.genes[i];secondFittest.genes[i] = temp;}}//Mutationvoid mutation() {Random rn = new Random();//Select a random mutation pointint mutationPoint = rn.nextInt(population.individuals[0].geneLength);//Flip values at the mutation pointif (fittest.genes[mutationPoint] == 0) {fittest.genes[mutationPoint] = 1;} else {fittest.genes[mutationPoint] = 0;}mutationPoint = rn.nextInt(population.individuals[0].geneLength);if (secondFittest.genes[mutationPoint] == 0) {secondFittest.genes[mutationPoint] = 1;} else {secondFittest.genes[mutationPoint] = 0;}}//Get fittest offspringIndividual getFittestOffspring() {if (fittest.fitness > secondFittest.fitness) {return fittest;}return secondFittest;}//Replace least fittest individual from most fittest offspringvoid addFittestOffspring() {//Update fitness values of offspringfittest.calcFitness();secondFittest.calcFitness();//Get index of least fit individualint leastFittestIndex = population.getLeastFittestIndex();//Replace least fittest individual from most fittest offspringpopulation.individuals[leastFittestIndex] = getFittestOffspring();}}//Individual class
class Individual implements Cloneable{int fitness = 0;int[] genes = new int[5];int geneLength = 5;public Individual() {Random rn = new Random();//Set genes randomly for each individualfor (int i = 0; i < genes.length; i++) {genes[i] = Math.abs(rn.nextInt() % 2);}fitness = 0;}//Calculate fitnesspublic void calcFitness() {fitness = 0;for (int i = 0; i < 5; i++) {if (genes[i] == 1) {++fitness;}}}@Overrideprotected Object clone() throws CloneNotSupportedException {Individual individual = (Individual)super.clone();individual.genes = new int[5];for(int i = 0; i < individual.genes.length; i++){individual.genes[i] = this.genes[i];}return individual;}
}//Population class
class Population {int popSize = 10;Individual[] individuals = new Individual[10];int fittest = 0;//Initialize populationpublic void initializePopulation(int size) {for (int i = 0; i < individuals.length; i++) {individuals[i] = new Individual();}}//Get the fittest individualpublic Individual getFittest() {int maxFit = Integer.MIN_VALUE;int maxFitIndex = 0;for (int i = 0; i < individuals.length; i++) {if (maxFit <= individuals[i].fitness) {maxFit = individuals[i].fitness;maxFitIndex = i;}}fittest = individuals[maxFitIndex].fitness;try {return (Individual) individuals[maxFitIndex].clone();} catch (CloneNotSupportedException e) {e.printStackTrace();}return null;}//Get the second most fittest individualpublic Individual getSecondFittest() {int maxFit1 = 0;int maxFit2 = 0;for (int i = 0; i < individuals.length; i++) {if (individuals[i].fitness > individuals[maxFit1].fitness) {maxFit2 = maxFit1;maxFit1 = i;} else if (individuals[i].fitness > individuals[maxFit2].fitness) {maxFit2 = i;}}try {return (Individual) individuals[maxFit2].clone();} catch (CloneNotSupportedException e) {e.printStackTrace();}return null;}//Get index of least fittest individualpublic int getLeastFittestIndex() {int minFitVal = Integer.MAX_VALUE;int minFitIndex = 0;for (int i = 0; i < individuals.length; i++) {if (minFitVal >= individuals[i].fitness) {minFitVal = individuals[i].fitness;minFitIndex = i;}}return minFitIndex;}//Calculate fitness of each individualpublic void calculateFitness() {for (int i = 0; i < individuals.length; i++) {individuals[i].calcFitness();}getFittest();}}

（例子2）使用python实现遗传算法的一个应用

本示例代码可以直接运行。给出一个目标句子，比如“Hello world, I am handsome!”，我们通过遗传算法，让随机初始化的句子群体进化出跟目标句子一样的个体。代码中包含详细的注释，此处不做过多解释！

# Python3 program to create target string, starting from
# random string using Genetic Algorithmimport random# Number of individuals in each generation
POPULATION_SIZE = 100# Valid genes
GENES = '''abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOP
QRSTUVWXYZ 1234567890, .-;:_!"#%&/()=?@${[]}'''# Target string to be generated
TARGET = "Hello world, I am handsome!"class Individual(object):'''Class representing individual in population'''def __init__(self, chromosome):self.chromosome = chromosomeself.fitness = self.cal_fitness()@classmethoddef cal_fitness(self):'''Calculate fittness score, it is the number ofcharacters in string which differ from targetstring.'''global TARGETfitness = 0for gs, gt in zip(self.chromosome, TARGET):if gs != gt: fitness += 1return fitness@classmethoddef mutated_genes(self):'''create random genes for mutation'''global GENESgene = random.choice(GENES)return gene@classmethoddef create_gnome(self):'''create chromosome or string of genes'''global TARGETgnome_len = len(TARGET)return [self.mutated_genes() for _ in range(gnome_len)]def mate(self, par2):'''Perform mating and produce new offspring'''# chromosome for offspringchild_chromosome = []for gp1, gp2 in zip(self.chromosome, par2.chromosome):# random probabilityprob = random.random()# if prob is less than 0.45, insert gene# from parent 1if prob < 0.45:child_chromosome.append(gp1)# if prob is between 0.45 and 0.90, insert# gene from parent 2elif prob < 0.90:child_chromosome.append(gp2)# otherwise insert random gene(mutate),# for maintaining diversityelse:child_chromosome.append(self.mutated_genes())# create new Individual(offspring) using# generated chromosome for offspringreturn Individual(child_chromosome)# Driver codedef main():global POPULATION_SIZE# current generationgeneration = 1found = Falsepopulation = []# create initial populationfor _ in range(POPULATION_SIZE):gnome = Individual.create_gnome()population.append(Individual(gnome))while not found:# sort the population in increasing order of fitness scorepopulation = sorted(population, key=lambda x: x.fitness)# if the individual having lowest fitness score ie.# 0 then we know that we have reached to the target# and break the loopif population[0].fitness <= 0:found = Truebreak# Otherwise generate new offsprings for new generationnew_generation = []# Perform Elitism, that mean 10% of fittest population# goes to the next generations = int((10 * POPULATION_SIZE) / 100)new_generation.extend(population[:s])# From 50% of fittest population, Individuals# will mate to produce offsprings = int((90 * POPULATION_SIZE) / 100)for _ in range(s):parent1 = random.choice(population[:50])parent2 = random.choice(population[:50])child = parent1.mate(parent2)new_generation.append(child)population = new_generationprint("Generation: {}\tString: {}\tFitness: {}". \format(generation,"".join(population[0].chromosome),population[0].fitness))generation += 1print("Generation: {}\tString: {}\tFitness: {}". \format(generation,"".join(population[0].chromosome),population[0].fitness))if __name__ == '__main__':main()

Reference

https://www.cnblogs.com/jingsupo/archive/2018/04/23/genetic-algorithm-python.html
https://www.analyticsvidhya.com/blog/2017/07/introduction-to-genetic-algorithm/
https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3

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