# 遗传算法的基本概念和实现（附 Java 实现案例）

By 机器之心2017年7月11日 10:32

#### 自然选择的概念

1. 初始化
2. 个体评价（计算适应度函数）
3. 选择运算
4. 交叉运算
5. 变异运算

#### 案例实现

START
Generate the initial population
Compute fitness
REPEAT
Selection
Crossover
Mutation
Compute fitness
UNTIL population has converged
STOP

Java 中的示例实现

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();
}

//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

//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 {

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] = rn.nextInt() % 2;
}

fitness = 0;
}

//Calculate fitness
public void calcFitness() {

fitness = 0;
for (int i = 0; i < 5; i++) {
if (genes[i] == 1) {
++fitness;
}
}
}

}

//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;
for (int i = 0; i < individuals.length; i++) {
if (maxFit <= individuals[i].fitness) {
maxFit = i;
}
}
fittest = individuals[maxFit].fitness;
return individuals[maxFit];
}

//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;
}
}
return individuals[maxFit2];
}

//Get index of least fittest individual
public int getLeastFittestIndex() {
int minFit = 0;
for (int i = 0; i < individuals.length; i++) {
if (minFit >= individuals[i].fitness) {
minFit = i;
}
}
return minFit;
}

//Calculate fitness of each individual
public void calculateFitness() {

for (int i = 0; i < individuals.length; i++) {
individuals[i].calcFitness();
}
getFittest();
}

}