cgroup.cs

C#编写的遗传算法程序

		for(i=1;i<PopSize;i++)
		{
			if(population[i].fitness>bestindividual.fitness)
			{
				getIndividual(bestindividual,population[i]);
				best_index=i;
			}
			else if(population[i].fitness<worstindividual.fitness)
			{
				getIndividual(worstindividual,population[i]);
				worst_index=i;
			}
			//	sum+=population[i].fitness;
		}
		//find out the best individual so far
		if(generation==0)
		{	//initialize the best individual
			getIndividual(currentbest,bestindividual);
		}
		else 
		{
			if(bestindividual.fitness>currentbest.fitness)
			{
				getIndividual(currentbest,bestindividual);
			}
		}
	}
	/*                    Function: To perform evolution operation based on dlitisemodel.
									Elitist model is to replace the worst individual of 
									this generation by the vurrent best one.
									Variable:None.                                     */
	void PerformEvolution()
	{
		if(bestindividual.fitness>currentbest.fitness)
		{
			getIndividual(currentbest,bestindividual);
		}
		else
		{
			getIndividual(population[worst_index],currentbest);
		}
	}
	/*                    Function: To reproduce a chromosome by proportional selection.
									Variable:None.                                       */
	void SelectionOperator()
	{
		int i,j,index;
		double p,sum=0.0;
		double[] cfitness=new double[POPSIZE];	//cumulative fitness value
		individual[] newpopulation=new individual[POPSIZE];
		for(i=0;i<POPSIZE;i++)
			newpopulation[i]=new individual();
		for(i=0;i<PopSize;i++)
		{
			for(j=0;j<CHROMLENGTH;j++)
			{
				//ASSERT(population[i].chrom[j]=='0'||population[i].chrom[j]=='1');
			}       
		}
		//calculate relative fitness	
		for(i=0;i<PopSize;i++)
		{
			sum+=population[i].fitness;
		}
		for(i=0;i<PopSize;i++)
		{
			cfitness[i]=population[i].fitness/sum;
		}

		//calculate cumulative fitness
		for(i=1;i<PopSize;i++)
		{
			cfitness[i]=cfitness[i-1]+cfitness[i];
		}    
		//selection operation
		for(i=0;i<PopSize;i++)
		{
			p=Rand(1.0);
			index=0;
			while(p-cfitness[index]>0.000001)
			{
				index++;
			}
			getIndividual(newpopulation[i],population[index]);
    
			for(j=0;j<CHROMLENGTH;j++)
			{
				//ASSERT(newpopulation[i].chrom[j]=='0'||newpopulation[i].chrom[j]=='1');
			}
		}
		for(i=0;i<PopSize;i++)
		{
			getIndividual(population[i],newpopulation[i]);
		}
		for(i=0;i<PopSize;i++)
		{
			for(j=0;j<CHROMLENGTH;j++)
			{
				//ASSERT(population[i].chrom[j]=='0'||population[i].chrom[j]=='1');
			}
    
		}
	}
	/*      Function:Crossover two chromosome by meansof one-point crossover.
						Variable:None.                                         */
	void CrossoverOperator()
	{
		int i,j;
		int[] index=new int[POPSIZE];
		int point,temp;
		double p;
		char ch;
		for(i=0;i<PopSize;i++)
		{
			for(j=0;j<CHROMLENGTH;j++)
			{
				//ASSERT(population[i].chrom[j]=='0'||population[i].chrom[j]=='1');
			}        
		}
		//make a pair of individual randomly
		for(i=0;i<PopSize;i++)
		{
			population[i].dirty=false;
			index[i]=i;
		}
		for(i=0;i<PopSize;i++)
		{
			point=(int)Math.Floor(Rand(PopSize-i));
			if(point==PopSize-i) point--;
			//ASSERT(point>=0&&point<PopSize-i) ;
			temp=index[i];
			index[i]=index[point+i];
			//ASSERT(index[i]<PopSize&&index[i]>=0);
			index[point+i]=temp;
			//ASSERT(index[point+i]<PopSize&&index[i]>=0);		  
		}
		//one-point crossover operation
		for(i=0;i<PopSize-1;i+=2)
		{
			p=Rand(1.0);
			if(p<Pc)
			{
				point=(int)Rand(CHROMLENGTH-1)+1;
				population[index[i]].dirty=population[index[i+1]].dirty=true;
				for(j=point;j<CHROMLENGTH;j++)
				{
					ch=population[index[i]].chrom[j];
					population[index[i]].chrom[j]=population[index[i+1]].chrom[j];
					population[index[i+1]].chrom[j]=ch;
				}
			}
		}
		for(i=0;i<PopSize;i++)
		{
			for(j=0;j<CHROMLENGTH;j++)
			{
				//ASSERT(population[i].chrom[j]=='0'||population[i].chrom[j]=='1');
		
			}
    
		}
	}

	/*                    Function:Mutation of a chromosome.
									Variable:None.                                */
	void MutationOperator()
	{
		int i,j;
		double p;
		//bit mutation
		for(i=0;i<PopSize;i++)
		{
			for(j=0;j<CHROMLENGTH;j++)
			{
				//ASSERT(population[i].chrom[j]=='0'||population[i].chrom[j]=='1');
			}
    
		}
		for(i=0;i<PopSize;i++)
		{
			for(j=0;j<CHROMLENGTH;j++)
			{
				p=Rand(1.0);
				if(p<Pm)
				{
					population[i].dirty=true;
					population[i].chrom[j]=(population[i].chrom[j]=='0')?'1':'0';
				}
			}
		}
		for(i=0;i<PopSize;i++)
		{
			for(j=0;j<CHROMLENGTH;j++)
			{
				//ASSERT(population[i].chrom[j]=='0'||population[i].chrom[j]=='1');
			}
    
		}
	}
	/*                    Function: Output the results of current population.
									Variable:None.									*/

	void Annealing()
	{
		individual[] newpopulation=new individual[POPSIZE];
		for(i=0;i<POPSIZE;i++)
			newpopulation[i]=new individual();
		double p,Pm_anneal=0.02;
		int temp3,temp4,temp1,temp2;
		double x1,x2,x3,x4,sum;
		for(int i=0;i<PopSize;i++)
		{
			getIndividual(newpopulation[i],population[i]);
			for(int j=0;j<CHROMLENGTH;j++)
			{
				p=Rand(1.0);
				if(p<Pm_anneal)
				{
					newpopulation[i].dirty=true;
					newpopulation[i].chrom[j]=(population[i].chrom[j]=='0')?'1':'0';
				}
			}
		}	
		//Rosenbrock function
		for(i=0;i<PopSize;i++)
		{
			if(newpopulation[i].dirty==true)
			{
				temp1=DecodeChromosome(newpopulation[i].chrom,0,LENGTH1);
				temp2=DecodeChromosome(newpopulation[i].chrom,LENGTH1,LENGTH2);
				temp3=DecodeChromosome(newpopulation[i].chrom,LENGTH1+LENGTH2,LENGTH3);
				temp4=DecodeChromosome(newpopulation[i].chrom,LENGTH1+LENGTH2+LENGTH3,LENGTH4);
				if(temp1==0)temp2=0;
				x1=temp1/20.0;
				x2=temp2/(double)Pointnum;
				x3=temp3/20.0;
				x4=2*PI*temp4/Pointnum;
				CalPopulation(i,x1,x2,x3,x4);
			}
		}
		for(i=0;i<PopSize;i++)
		{
			if(newpopulation[i].dirty==true)
			{
				newpopulation[i].fitness=Math.Pow(newpopulation[i].value,2);
				newpopulation[i].dirty=false;
			}
			if(Math.Abs(newpopulation[i].value)>=Math.Abs(population[i].value))
			{
				getIndividual(population[i],newpopulation[i]);
			}
		}
		sum=0.0;
		for(i=0;i<PopSize;i++)
		{
			sum+=population[i].fitness;
		}
		averagevalue=sum/PopSize;
		sum=0.0;
		for(i=0;i<PopSize;i++)
		{
			sum+=Math.Pow(population[i].fitness-averagevalue,2);
		}
		fangcha=Math.Sqrt(sum/PopSize)/averagevalue;
	}

	double Rand(double rank)
	{
		double i=random.NextDouble();
		return i*rank;

	} 

	double impulse(double tn,double a,double b,double sita,double fai)
	{
		if(tn<b) return 0.0;
		return Math.Exp(-a*(tn-b))*Math.Cos(2*PI*sita*tn+fai);
	}

	void getIndividual(individual Des,individual Src)
	{
		int i;
		for(i=0;i<CHROMLENGTH;i++)
			Des.chrom[i]=Src.chrom[i];
		Des.value=Src.value;
		Des.fitness =Src.fitness ;
		Des.dirty =Src.dirty;
	}
}