ga.cpp

遗传算法

#include<iostream>
#include<math.h>
using namespace std;
const int MAXGENE=20;
const int MAXPOPSIZE=500;
const int EPOCH=50;
const double Pc=0.7;
const double Pm=0.1;
double OptFun(double x,double y)
{
	//return 100*(x*x-y)*(x*x-y)+(1-x)*(1-x);
	return x*x;
}
class Chormosome
{
public:
	int Gene[MAXGENE];
	double Fitness;
	void CalculateFitness();
	Chormosome()
	{
		for(int i=0;i<MAXGENE;i++)
			Gene[i]=rand()%2;
	}
};
void Chormosome::CalculateFitness()
{
	double x=0,y=0;
	for(int i=0;i<MAXGENE/2;i++)
	{
		x=x+Gene[i]*pow(2,i);
		y=y+Gene[i+MAXGENE/2]*pow(2,i);
	}
	x=(4*x)/1023-2;
	y=(4*y)/1023-2;
/*	x=(4.096*x)/1023-2.048;
	y=(4.096*y)/1023-2.048;//*/
	Fitness=OptFun(x,y);
}

class Population
{
public:
	double dbestFitness;
	Chormosome chorm[MAXPOPSIZE];
	void Selection();
	void Crossover();
	void Mutation();
	void Evolution();//*/
	void Inital();
};

void Population::Selection()
{
	Chormosome chormtemp[MAXPOPSIZE];
	double dFitnessTotal=0,dFitnessFractile[MAXPOPSIZE];
	int i;
	for(i=0;i<MAXPOPSIZE;i++)
		dFitnessTotal+=chorm[i].Fitness;
	for(i=0;i<MAXPOPSIZE;i++)
		dFitnessFractile[i]=chorm[i].Fitness/dFitnessTotal;
	for(i=0;i<MAXPOPSIZE;i++)
	{
		double p=(double)rand()/RAND_MAX;
		double ptemp=0;
		for(int j=0;j<MAXPOPSIZE;j++)
		{
			ptemp+=dFitnessFractile[j];
			if(p<ptemp)
			{chormtemp[i]=chorm[j];break;}
		}
	}
	for(i=0;i<MAXPOPSIZE;i++)
		chorm[i]=chormtemp[i];
}

void Population::Crossover()
{
	Chormosome temp;
	int i;
	for(i=0;i<MAXPOPSIZE;i++)
	{
		int j=rand()%(MAXPOPSIZE);
		temp=chorm[i];chorm[i]=chorm[j];chorm[j]=temp;
	}
	for(i=0;i<MAXPOPSIZE;i+=2)
	{
		double p=(double)rand()/RAND_MAX;
		if(p<Pc)
		{
			for(int j=0;j<MAXGENE/2;j++)
			{
				int inttemp;
				inttemp=chorm[i].Gene[j];
				chorm[i].Gene[j]=chorm[i+1].Gene[j];
				chorm[i+1].Gene[j]=inttemp;
			}
		}
	}
}

void Population::Mutation()
{
	int i;
	for(i=0;i<MAXPOPSIZE;i++)
	{
		double p=(double)rand()/RAND_MAX;
		if(p<Pm)
		{
			int Pos=rand()%MAXGENE;
			if(chorm[i].Gene[Pos]==0)
				chorm[i].Gene[Pos]=1;
			else chorm[i].Gene[Pos]=0;
		}
	}
}

void Population::Evolution()
{
	int curgene=0;double dgoalFitness=0;
	while(curgene<EPOCH)
	{
		Population::Selection();
		Population::Crossover();
		Population::Mutation();
		Population::Inital();
		curgene++;
		if(dgoalFitness<dbestFitness)dgoalFitness=dbestFitness;
		cout<<"第"<<curgene<<"代"<<"Fitness:"<<dbestFitness<<endl;
		cout<<"全局最好值:"<<dgoalFitness<<endl;
	}
}

void Population::Inital()
{
	dbestFitness=0;
	for(int i=0;i<MAXPOPSIZE;i++)
	{	
		chorm[i].CalculateFitness();
		if(dbestFitness<chorm[i].Fitness)
			dbestFitness=chorm[i].Fitness;
	}
}

void main()
{
	Population Pop;
	Pop.Evolution();
}