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
（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 population
demo.population.initializePopulation(10);
//Calculate fitness of each individual
demo.population.calculateFitness();
System.out.println("Generation: " + demo.generationCount + " Fittest: " + demo.population.fittest);
//While population gets an individual with maximum fitness
while (demo.population.fittest < 5) {
++demo.generationCount;
//Do selection
demo.selection();
//Do crossover
demo.crossover();
//Do mutation under a random probability
if (rn.nextInt()%7 < 5) {
demo.mutation();
}
//Add fittest offspring to population
demo.addFittestOffspring();
//Calculate new fitness value
demo.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("");
}
//Selection
void selection() {
//Select the most fittest individual
fittest = population.getFittest();
//Select the second most fittest individual
secondFittest = population.getSecondFittest();
}
//Crossover
void crossover() {
Random rn = new Random();
//Select a random crossover point
int crossOverPoint = rn.nextInt(population.individuals[0].geneLength);
//Swap values among parents
for (int i = 0; i < crossOverPoint; i++) {
int temp = fittest.genes[i];
fittest.genes[i] = secondFittest.genes[i];
secondFittest.genes[i] = temp;
}
}
//Mutation
void mutation() {
Random rn = new Random();
//Select a random mutation point
int mutationPoint = rn.nextInt(population.individuals[0].geneLength);
//Flip values at the mutation point
if (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 offspring
Individual getFittestOffspring() {
if (fittest.fitness > secondFittest.fitness) {
return fittest;
}
return secondFittest;
}
//Replace least fittest individual from most fittest offspring
void addFittestOffspring() {
//Update fitness values of offspring
fittest.calcFitness();
secondFittest.calcFitness();
//Get index of least fit individual
int leastFittestIndex = population.getLeastFittestIndex();
//Replace least fittest individual from most fittest offspring
population.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 individual
for (int i = 0; i < genes.length; i++) {
genes[i] = Math.abs(rn.nextInt() % 2);
}
fitness = 0;
}
//Calculate fitness
public void calcFitness() {
fitness = 0;
for (int i = 0; i < 5; i++) {
if (genes[i] == 1) {
++fitness;
}
}
}
@Override
protected 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 population
public void initializePopulation(int size) {
for (int i = 0; i < individuals.length; i++) {
individuals[i] = new Individual();
}
}
//Get the fittest individual
public 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 individual
public 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 individual
public 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 individual
public 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 Algorithm
import 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 = chromosome
self.fitness = self.cal_fitness()
@classmethod
def cal_fitness(self):
'''
Calculate fittness score, it is the number of
characters in string which differ from target
string.
'''
global TARGET
fitness = 0
for gs, gt in zip(self.chromosome, TARGET):
if gs != gt: fitness += 1
return fitness
@classmethod
def mutated_genes(self):
'''
create random genes for mutation
'''
global GENES
gene = random.choice(GENES)
return gene
@classmethod
def create_gnome(self):
'''
create chromosome or string of genes
'''
global TARGET
gnome_len = len(TARGET)
return [self.mutated_genes() for _ in range(gnome_len)]
def mate(self, par2):
'''
Perform mating and produce new offspring
'''
# chromosome for offspring
child_chromosome = []
for gp1, gp2 in zip(self.chromosome, par2.chromosome):
# random probability
prob = random.random()
# if prob is less than 0.45, insert gene
# from parent 1
if prob < 0.45:
child_chromosome.append(gp1)
# if prob is between 0.45 and 0.90, insert
# gene from parent 2
elif prob < 0.90:
child_chromosome.append(gp2)
# otherwise insert random gene(mutate),
# for maintaining diversity
else:
child_chromosome.append(self.mutated_genes())
# create new Individual(offspring) using
# generated chromosome for offspring
return Individual(child_chromosome)
# Driver code
def main():
global POPULATION_SIZE
# current generation
generation = 1
found = False
population = []
# create initial population
for _ in range(POPULATION_SIZE):
gnome = Individual.create_gnome()
population.append(Individual(gnome))
while not found:
# sort the population in increasing order of fitness score
population = 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 loop
if population[0].fitness <= 0:
found = True
break
# Otherwise generate new offsprings for new generation
new_generation = []
# Perform Elitism, that mean 10% of fittest population
# goes to the next generation
s = int((10 * POPULATION_SIZE) / 100)
new_generation.extend(population[:s])
# From 50% of fittest population, Individuals
# will mate to produce offspring
s = 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_generation
print("Generation: {}\tString: {}\tFitness: {}". \
format(generation,
"".join(population[0].chromosome),
population[0].fitness))
generation += 1
print("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
