应用于TSP问题，对其他NP问题可能需要改变编码策略，以下是C++实现

/*由Parrot改进的遗传算法求解TSP
*
*选择操作使用转赌轮算法
*变异算子采用逆转变异算法
*交叉时先使用顺序编码，单点交叉，然后解码 
*/

#include <iostream>
#include <fstream>
#include <vector>
#include <algorithm>
#include <cstdlib>
#include<unordered_map>
#include<utility>
#include <time.h>
#include <assert.h>
using namespace std;

unsigned int cityNum=20; //城市数目
double** dist;  //两两城市之间距离
const unsigned int generation = 5000;	 //迭代次数
const unsigned int populationSize = 50;  //染色体数目
unsigned int chromLen = cityNum;   //单条染色体序列长度，在这里为城市数目cityNum

const double mutationRate = 0.07;  //基因突变概率
const double crossoverRate = 0.95;	 //两条染色体交叉概率
const int select_chromPair = 30; //选择染色体交叉对的次数
//等级选择+轮盘赌算法
//无条件保留最优的5条解，其余的分成5个等级，选择rank[5,10)的概率为2%
//选择rank[10,20)的概率为10%, 选择rank[20,30)的概率为18%
//选择rank[30,40)的概率为30%，选择rank[40,50)的概率为40%
//每个等级内部则根据其适应函数按轮盘赌算法选择

//轮盘赌算法，用于等级算法的第二次选择
int roulette_select(const double *fitness,int p,int r)
{
	int i;
	double* stage=new double[r-p+1];
	stage[0] = fitness[p];
	for (i=1;i<r-p+1;i++){
		stage[i]=stage[i-1] + fitness[p+i]; 
	}
	double d = (rand()*1.0/RAND_MAX) * stage[r-p];
	i=0;
	while(d>stage[i]) i++;
	delete[] stage;
	return p+i;
}

int rank_select(const double* fitness,int sz)
{
	int i;
	//第一次选择等级区间
	int rand_num = rand()%100;
	if(rand_num<2) return roulette_select(fitness,5,9);
	if(rand_num<12) return roulette_select(fitness,10,19);
	if(rand_num<30) return roulette_select(fitness,20,29);
	if(rand_num<60) return roulette_select(fitness,30,39);
	return roulette_select(fitness,40,49);
}

//生成[0,n-1]的随机序列,保存到randorder中
void rand_int_order(vector<int>& randorder,const int n)   
{   
	assert(n>1);  

	randorder.clear();
	randorder.resize(n);
	vector<int> recoder;
	for (int i=0;i<n;i++){
		recoder.push_back(i);	
	}
	for(int i=0;i<n;i++){
		int sel=rand()%recoder.size();
		randorder[i]=recoder[sel];
		recoder.erase(recoder.begin()+sel); 
	}
	//assertion
	assert(randorder.size()==n);
}

//编码函数
void sequence_encode(vector<int>& chromosome)
{
	vector<int> standard;
	for (int i=0;i<chromosome.size();i++){
		standard.push_back(i); 
	}
	vector<int>::iterator ite;
	for (int i=0;i<chromosome.size();i++){
		ite=find(standard.begin(),standard.end(),chromosome[i]);
		assert(ite!=standard.end());
		chromosome[i]=ite-standard.begin();
		standard.erase(ite); 
	}

}
//解码函数
void sequence_decode(vector<int>& chromosome)
{
	vector<int>standard;
	for (int i=0;i<chromosome.size();i++){
		standard.push_back(i); 
	}
	for (int i=0;i<chromosome.size();i++){	
		int t=chromosome[i];
		chromosome[i]=standard[t]; 
		standard.erase(standard.begin()+t);
	}

}

//计算可行路径的适应函数值
double caculate_fit(const vector<int> &path)
{
	double sum=0;
	for(int i=1;i<=chromLen;i++) sum+=dist[path[i-1]][path[i%chromLen]];
	return sum;
}

//初始化各城市之间的距离
void init(){
	fstream fin("input_data.txt",ios::in);
	dist=new double*[cityNum];
	for (int i=0;i<cityNum;i++)	dist[i]=new double[cityNum];
	int data;
	for (int i=0;i<cityNum;i++){		
		for (int j=i;j<cityNum;j++){
			fin>>data;
			if(i!=j) dist[i][j]=dist[j][i]=data;
			else dist[i][j]=0;
		}
	}
	fin.close();
}

void run()
{	
	fstream fout("output.txt",ios::out);
	int curTime=int(time(0));

	srand((unsigned)time(NULL));

	// 染色体数组
	vector<vector<int> > population; //第0代

	//随机化染色体序列
	for (int i=0;i<populationSize;i++){
		vector<int> chromo; 
		rand_int_order(chromo,chromLen); //随机产生一个可行解（用chromo表示）
		population.push_back(chromo); 
	} 

	double glob_best_len = INT_MAX;  //整个进化过程中得到的最优路径的长度
	vector<int> glob_best_path; //整个进化过程中得到的最优路径

	//开始进化
	for (int g=0;g<generation;g++){	
		double *fitness = new double[populationSize];	//适应函数（可行路径的长度）
		for(int i=0;i<populationSize;i++) fitness[i]=0;
		//计算当前每条染色体的适应度函数（路径的长度）
		for (int s=0;s<populationSize;s++) fitness[s]=caculate_fit(population[s]); 
			
		vector<vector<int> > parent;   //parent为population按适应函数插入排序后得到的染色体组
		for(int i=0;i<population.size();i++){
			int j=0;  
			while(j<parent.size() && fitness[i]>=fitness[j]) j++;  //插入位置
			parent.insert(parent.begin()+j,population[i]);
			double tmp = fitness[i]; 
			for(int k=i;k>=j+1;k--) fitness[k]=fitness[k-1];    //同时更改fitness
			fitness[j]=tmp;  
		}

		population.clear(); //清空population，用以保存下一轮染色体
		fout<<"**************************************************************************\n";
		fout<<"第 "<<g<<" 轮进化\n";
		for(int i=0;i<populationSize;i++){
			fout<<"路径"<<i<<":"<<fitness[i]<<"(长度）\n";
			for(int j=0;j<chromLen;j++) fout<<parent[i][j]<<"-->";
			fout<<"\n\n";
		}
		fout<<"**************************************************************************\n";

		if(glob_best_len>fitness[0]){ glob_best_len=fitness[0]; glob_best_path = parent[0];} 

		//选择25对交叉的染色体序号，去掉重复的
		unordered_map<int,int> tab;   //交叉对
		for(int i=0;i<select_chromPair;i++){
			int rs1 = rank_select(fitness,populationSize-1),rs2=rs1;
			while(rs2==rs1) rs2 = rank_select(fitness,populationSize-1);
			if(tab.find(rs1)==tab.end() && tab.find(rs2)==tab.end()){
				tab[rs1] = rs2; tab[rs2] = rs1;
			}
		}
		
		//将当前代没有被选择作为交叉对的直接加入到下一代
		for(int i=0;i<populationSize;i++){
			if(tab.find(i)==tab.end()){
				vector<int> baby = parent[i];
				if(rand()*1.0/RAND_MAX < mutationRate){ //进行变异
					int p1 = rand()%chromLen, p2 = p1;
					while(p1==p2) p2=rand()%chromLen;

					//逆转baby染色体的某一段 
					reverse(baby.begin()+ min(p1,p2),baby.begin()+ max(p1,p2));
				}
				//精英策略，若是比当前代更差，则不保留baby
				if(caculate_fit(baby)<fitness[i]) population.push_back(baby);
				else population.push_back(parent[i]);
				
			}
		}
		//按交叉概率决定选出来的交叉对是否交叉
		while(population.size()<populationSize){

			unordered_map<int,int>::iterator it = tab.begin();
			int k1 = it->first, k2 = it->second;
			vector<int> baby1 = parent[k1], baby2 = parent[k2];
			tab.erase(k1);
			tab.erase(k2);
			if (rand()*1.0/RAND_MAX<crossoverRate){ //染色体交叉
				//编码
				sequence_encode(baby1);
				sequence_encode(baby2);

				//将编码后的两条染色体单点交叉
				int crs = rand()%chromLen;
				swap(baby1[crs],baby2[crs]);

				//解码,解码后的染色体可能是连续交叉
				sequence_decode(baby1);
				sequence_decode(baby2);	
			}

			//根据变异概率让baby1,baby2变异
			for(int b=0;b<=1;b++){
				if(rand()*1.0/RAND_MAX < mutationRate){
					int p1 = rand()%chromLen, p2 = p1;
					while(p1==p2) p2=rand()%chromLen;

					//逆转baby1,baby2染色体的某一段 
					if(b==0) reverse(baby1.begin()+ min(p1,p2),baby1.begin()+ max(p1,p2));
					else reverse(baby2.begin()+ min(p1,p2),baby2.begin()+ max(p1,p2));
				}
			}
			
			if(caculate_fit(baby1)<fitness[k1]) population.push_back(baby1);
			else population.push_back(parent[k1]);
			if(caculate_fit(baby2)<fitness[k2]) population.push_back(baby2);
			else population.push_back(parent[k2]);	
		}	
		delete fitness;
	}

	fout<<"\n\n\n历史最优路径长度: "
		<<glob_best_len<<endl			
		<<"历史最优路径:\n";
	for (int i=0;i<cityNum;i++){
		fout<<glob_best_path[i]<<" ";
	}
	fout<<endl;
	curTime=int(time(0))-curTime;
	fout<<"总共用时:"<<curTime<<"ms\n";
	fout.close();
}

int main(){
	cout<<"running..."<<endl;
	init();
	run(); 
	for (int i=0;i<cityNum;i++){ delete[] dist[i];}	
	delete[] dist;
	cout<<"done!"<<endl;
	return 0;
}