///////////////////////////////////////////////////////////////////
/*****************************************************************
Copyright: Artificial Life and Adaptive Robotics Laboratory - ALAR
School of ITEE, UNSW@ADFA, Australia, 2005
*******************************************************************/
///////////////////////////////////////////////////////////////////#include "population.h"//////////////////////////////////////////////////////////////////////// Construction/Destruction//////////////////////////////////////////////////////////////////////population::population(){	pop_size=0;	individuals=NULL;	//rank=NULL;}population::population(int size){	individuals=NULL;	//rank=NULL;	set_pop_size(size);}population::~population(){		if (individuals) {	delete [] individuals;	individuals=NULL;}			//if (rank) {	delete [] rank;	rank=NULL;}	}int  population::check_dominationN(individual &id1, individual &id2){	int i;	int check1=TRUE,check2=FALSE;	for(i=0;i<id1.InumberofObjs;i++)	{		if (id1.DfitnessN[i]>id2.DfitnessN[i])		{			check1=FALSE;		}		else if (id1.DfitnessN[i]<id2.DfitnessN[i])		{			check2=TRUE;		}	}	return check1&&check2;}int  population::check_dominationC(individual &id1, individual &id2){	int i;	int check1=TRUE,check2=FALSE;	for(i=0;i<id1.InumberofObjs;i++)	{		if (id1.DfitnessC[i]>id2.DfitnessC[i])		{			check1=FALSE;		}		else if (id1.DfitnessC[i]<id2.DfitnessC[i])		{			check2=TRUE;		}	}	return check1&&check2;}int  population::check_ParetoOptimalN(individual &id1,int level){	int i,check=TRUE;	for(i=0;i<pop_size;i++)	{		if ((individuals[i].rank==-1)||(individuals[i].rank==level))			if (check_dominationN(individuals[i],id1))			{				check=FALSE;				break;			}	}	return check;}int  population::check_ParetoOptimalC(individual &id1,int level){	int i,check=TRUE;	for(i=0;i<pop_size;i++)	{		if ((individuals[i].rank==-1)||(individuals[i].rank==level))			if (check_dominationC(individuals[i],id1))			{				check=FALSE;				break;			}	}	return check;}void population::statistic(int g,ofstream &f){	cal_averagefitness();	cal_standarddeviation();	//population	print(g,f);	//print_ParetoOptimal(f);}void population::rankingC(){	int i,check=TRUE,level=0;		for (i=0;i<pop_size;i++)		individuals[i].rank=-1;
		while (1)	{		check=TRUE;		for (i=0;i<pop_size;i++)		{			if (individuals[i].rank==-1)			{				if (check_ParetoOptimalC(individuals[i],level))				{					individuals[i].rank=level;				}				check=FALSE;			}		}				if (check) break;		level++;	}		//rank store	rank_level=level;	}void population::rankingN(){	int i,check=TRUE,level=0;		for (i=0;i<pop_size;i++)		individuals[i].rank=-1;	while (1)	{		check=TRUE;		for (i=0;i<pop_size;i++)		{						if (individuals[i].rank==-1)			{				if (check_ParetoOptimalN(individuals[i],level))				{					individuals[i].rank=level;				}				check=FALSE;			}		}				if (check) break;		level++;	}		//rank store	rank_level=level;	}void population::cal_averagefitness(){	average_fitness=0;	for (int i=0;i<pop_size;i++)		average_fitness +=individuals[i].DglobalFitness;	average_fitness=average_fitness/pop_size;}void population::cal_standarddeviation(){	double s1=0,s2=0,temp=0;	for (int i=0;i<pop_size;i++)	{		temp=individuals[i].DglobalFitness;		s1+=temp*temp;		s2+=temp;	}	standard_deviation=(pop_size*s1-s2*s2)/(pop_size*(pop_size-1));}void population::set_pop_size(int s){	if (s!=pop_size)	{				pop_size=s;		if (individuals) {delete [] individuals; individuals=NULL;}		individuals= new individual[s];						for (int i=0;i<pop_size;i++)			individuals[i].rank=-1;	}}void population::print(int g, ofstream &f){	int i;	f<< "-----------GENERATION: " << g << " pop size: "<< pop_size << "--------------------------------------------------------" << endl;		for (i=0;i<pop_size;i++)		individuals[i].print_fn(f); 	//individuals[i].print_x(f); }void population::print_ParetoOptimal_fn(int g, ofstream &f,ofstream &f1){	//int c=0;	n_nondominated_set=0;	//	f << g << endl;	double min=D_MAX;    int i;	for (i=0;i<pop_size;i++)	{
		if (individuals[i].rank==0)		{			//individuals[i].print_fn(f);			n_nondominated_set++;			if (individuals[i].DfitnessN[1]<min) min=individuals[i].DfitnessN[1];		}	}	f1 << setiosflags(ios::fixed|ios::showpoint) << setprecision(3) << min << endl;		//f << "number of pareto set: " << n_nondominated_set << endl;}void population::print_ParetoOptimal_x(int g, ofstream &f){	//f << g << endl;	for (int i=0;i<pop_size;i++)		if (individuals[i].rank==0)		{			individuals[i].print_x(f);		}}

double population::diversity()
{
	int chromi,j;//,k;//,l;
	double diversity=0.0;
	int tt=pop_size*(pop_size-1)/2;
	for (chromi=0;chromi<pop_size-1;chromi++)
	{
		for (j=chromi+1;j<pop_size;j++)
			diversity += eDistance(individuals[chromi],individuals[j]);
	}
	return(diversity/(double)tt);
}

double population::eDistance(individual &id1, individual &id2)
{
	
	int i,N=id1.InumberofGenes;
	double temp=0;
	for (i=0;i<N;i++)
		temp+=pow((id1.Dgenes[i]-id2.Dgenes[i])/(id2.Dupper[i]-id2.Dlower[i] ),2) ;
	return sqrt(temp);
}

population &population::operator=(const population &pop){	int i;		set_pop_size(pop.pop_size);		rank_level			=	pop.rank_level;  	n_nondominated_set	=	pop.n_nondominated_set;		if (pop.individuals) 	{		for (i=0;i<pop_size;i++)			individuals[i] = pop.individuals[i]; 	}		for (i=0;i<pop_size;i++)		individuals[i].rank=pop.individuals[i].rank; 	return *this;		}