/* 
 * File:   GA_Minimal.cpp
 * Author: GonewithGt
 *
 */
#include <iostream>
#include <cstdlib>
#include <cmath>
#include <ctime>
#include <cstdint>
#include <cassert>
#include <algorithm>
#include <iomanip>
#define POP_SIZE        (100)
#define Genius          (20)
#define MAX_GENR        (100000)
#define MUT_CHANCE      (10)
#define GENE_TYPE       double
#define GENE_SIZE	(sizeof(GENE_TYPE))
//#define GENE_MASK       (GENE_SIZE/2)
using namespace std;
typedef struct {
    GENE_TYPE gene;
	GENE_TYPE gene2;
    double fitness;
} individual; 
individual population[POP_SIZE];
//GENE_TYPE mutRate = MUT_CHANCE;
double function2(double x){
	double tmp=0.;
	for(int j=1;j<=5;j++)
	{
		tmp=j*1.0*cos((j+1)*x*1.0+j)+tmp;
	}
	return tmp;
}
double fitnessFunction2(GENE_TYPE x) {
    if (x > 1e-8)
		return function2(x);
	//return x * x * log(x);
    return 0.; // limit of above function around 0
}
void recombine2(GENE_TYPE p1, GENE_TYPE p2, GENE_TYPE *c1, GENE_TYPE *c2) {
    assert(sizeof(uint64_t) == sizeof(GENE_TYPE));
    int cpoint = rand() % (GENE_SIZE * 8) + 1;
    uint64_t mask = (1UL << cpoint) - 1;
    
    *(uint64_t *)(c1) = *(uint64_t *)(&p1) & (mask);
    *(uint64_t *)(c1) |= *(uint64_t *)(&p2) & (mask);
    *(uint64_t *)(c2) = *(uint64_t *)(&p2) & (mask);
    *(uint64_t *)(c2) |= *(uint64_t *)(&p1) & (mask);
}
void mutate2(GENE_TYPE * c1, GENE_TYPE * c2) {
    *c1 = rand()*20./RAND_MAX-10.;
    *c2 = rand()*20./RAND_MAX-10.;
}
bool ValueCmp2(individual const & a, individual const & b) {
    return a.fitness < b.fitness;
}
/*
 * MinimalGA uygulamasi ana fonksiyonu
 */
int main(int argc, char** argv) {
	//freopen("Uva673.in","r",stdin);
	freopen("test26.out","w",stdout);
	cout<<"Generic 2"<<endl;
    int i, j;
    GENE_TYPE parent1, parent2, parent21, parent22;
    GENE_TYPE child1, child2, child21, child22;
    srand(time(0));
    
    //cout << "Basit Genetik Algoritma Uygulamasi" << endl;
    //cout << "Uyumluluk fonksiyonu : Some exponential stuff" <<GENE_SIZE<< endl;
    cout << "Population Size : " << POP_SIZE << endl;
    cout << "Max Generation: " << MAX_GENR << endl;
	cout << "Genius: " << Genius << endl;
    //cout << "Mutasyon olasiligi : " << MUT_CHANCE << endl;
    
    for (i = 0; i < POP_SIZE; i++) {
        //population[i].gene = (rand() % 100 - 50) / 50.;
		population[i].gene = rand()*20./RAND_MAX-10.;
		population[i].gene2 = rand()*20./RAND_MAX-10.;
        population[i].fitness = fitnessFunction2(population[i].gene)*fitnessFunction2(population[i].gene2);
    }
    sort(population, population + POP_SIZE, ValueCmp2);
    
    i = 0;
    while( i < MAX_GENR) {         
        for (j = Genius; j < POP_SIZE-Genius; j += 2) {
            parent1 = population[j].gene;
            parent2 = population[j + 1].gene;
            if (j < rand()%POP_SIZE) 
				mutate2(&child1, &child2);
			else
				recombine2(parent1, parent2, &child1, &child2);
            population[j].gene = child1;
            population[j + 1].gene = child2;
			parent21 = population[j].gene2;
            parent22 = population[j + 1].gene2;
            if (j < rand()%POP_SIZE) 
				mutate2(&child21, &child22);
			else
				recombine2(parent21, parent22, &child21, &child22);
            population[j].gene2 = child21;
            population[j + 1].gene2 = child22;
        }
		for (j = POP_SIZE-Genius; j < POP_SIZE-10; j += 2) {
            parent1 = population[j-(POP_SIZE-Genius)].gene;
            parent2 = population[j + 1-(POP_SIZE-Genius)].gene;
            if (j < rand()%POP_SIZE) 
				mutate2(&child1, &child2);
			else
				recombine2(parent1, parent2, &child1, &child2);
            population[j].gene = child1;
            population[j + 1].gene = child2;
			parent21 = population[j].gene2;
            parent22 = population[j + 1].gene2;
            if (rand()%POP_SIZE<0.3*POP_SIZE) 
				mutate2(&child21, &child22);
			else
				recombine2(parent21, parent22, &child21, &child22);
            population[j].gene2 = child21;
            population[j + 1].gene2 = child22;
		}
		for (j = POP_SIZE-10; j < POP_SIZE; j += 2) {
            parent1 = population[j-(POP_SIZE-10)].gene;
            parent2 = population[j + 1-(POP_SIZE-10)].gene;
            if (j < rand()%POP_SIZE) 
				mutate2(&child1, &child2);
			else
				recombine2(parent1, parent2, &child1, &child2);
            population[j].gene = child1;
            population[j + 1].gene = child2;
			//parent21 = population[j].gene2;
            //parent22 = population[j + 1].gene2;
            //if (rand()%POP_SIZE<0.3*POP_SIZE) 
			//	mutate2(&child21, &child22);
			//else
			//	recombine2(parent21, parent22, &child21, &child22);
            //population[j].gene2 = child21;
           // population[j + 1].gene2 = child22;
        }
        
        for(j = 0; j < POP_SIZE; j++)
            population[j].fitness = fitnessFunction2(population[j].gene)*fitnessFunction2(population[j].gene2);
        
        sort(population, population + POP_SIZE, ValueCmp2);
        
	//mutRate *= 0.9999;
        //if(abs(population[0].fitness) < 1e-8 || abs(population[1].fitness) < 1e-8)
        //    break;
			cout<<"The "<<i<<"th generation:"<<endl;
		for(int k=0;k<20;k++){
		
		cout<<setprecision(9)<<"  x"<<k<<1<<"="<<population[k].gene<<"  x"<<k<<2<<"="<<population[k].gene2<<"  y="<<population[k].fitness<<endl;
		
	}
		i++;
    }
    
    //int minIndex = (population[0].fitness > population[1].fitness) ? 1 : 0;
    //cout << "An effective gen : " << population[minIndex].gene << endl;
    //cout << "Minimal fitness : " << population[minIndex].fitness << endl;
    
   // cin.get();
    
    return 0;
}