main.cpp

遗传算法的算法编程框架,包括编码,选择,交叉,变异等等,可执行,并赋有一定的注释,可以在基于此框架上编写自己的算法流程

/***************************************************************/
/* This is a simple genetic algorithm implementation where the */
/* evaluation function takes positive values only and the      */
/* fitness of an individual is the same as the value of the    */
/* objective function                                          */
/***************************************************************/

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

/* Change any of these parameters to match your needs */

#define POPSIZE 100               /* population size */
#define MAXGENS 500             /* max. number of generations */
#define NVARS 2                 /* no. of problem variables */
#define PXOVER 0.8               /* probability of crossover */
#define PMUTATION 0.001           /* probability of mutation */
#define TRUE 1
#define FALSE 0

int generation;                  /* current generation no. */
int cur_best;                    /* best individual */
FILE *galog;                     /* an output(1) file */
FILE *output;                     /* an output(1) file */

struct genotype /* genotype (GT), a member of the population */
{
	double gene[NVARS];        /* a string of variables */
	double fitness;            /* GT's fitness */
	double upper[NVARS];       /* GT's variables upper bound */
	double lower[NVARS];       /* GT's variables lower bound */
	double rfitness;           /* relative fitness */
	double cfitness;           /* cumulative fitness */
};

struct genotype population[POPSIZE+1];    /* population */
struct genotype newpopulation[POPSIZE+1]; /* new population; */
/* replaces the */
/* old generation */

/* Declaration of procedures used by this genetic algorithm */

void initialize(void);
double randval(double, double);
void evaluate(void);
void keep_the_best(void);
void elitist(void);
void select(void);
void crossover(void);
void Xover(int,int);
void swap(double *, double *);
void mutate(void);
void report(void);

/***************************************************************/
/* Initialization function: Initializes the values of genes    */
/* within the variables bounds. It also initializes (to zero)  */
/* all fitness values for each member of the population. It    */
/* reads upper and lower bounds of each variable from the      */
/* input file `gadata.txt'. It randomly generates values       */
/* between these bounds for each gene of each genotype in the  */
/* population. The format of the input file `gadata.txt' is    */
/* var1_lower_bound var1_upper bound                           */
/* var2_lower_bound var2_upper bound ...                       */
/***************************************************************/

void initialize(void)
{
	FILE *infile;
	int i, j;
	double lbound, ubound;

	if ((infile = fopen("gadata.txt","r"))==NULL)
	{
		fprintf(galog,"\nCannot open input file!\n");
		exit(1);
	}

	remove("output.dat");
	/* initialize variables within the bounds */

	for (i = 0; i < NVARS; i++)
	{
		fscanf(infile, "%lf",&lbound);
		fscanf(infile, "%lf",&ubound);


		for (j = 0; j < POPSIZE; j++)
		{
			population[j].fitness = 0;
			population[j].rfitness = 0;
			population[j].cfitness = 0;
			population[j].lower[i] = lbound;
			population[j].upper[i] = ubound;
			population[j].gene[i]  = randval(population[j].lower[i],
				population[j].upper[i]);
		}
	}

	fclose(infile);
}

/***********************************************************/
/* Random value generator: Generates a value within bounds */
/***********************************************************/

double randval(double low, double high)
{
	double val;
	val = ((double)(rand()%1000)/1000.0)*(high - low) + low;
	return(val);
}

/*************************************************************/
/* Evaluation function: This takes a user defined function.  */
/* Each time this is changed, the code has to be recompiled. */
/* The current function is:  x[1]^2-x[1]*x[2]+x[3]           */
/*************************************************************/

void evaluate(void)
{
	if ((output = fopen("output.dat","a"))==NULL)
	{
		exit(1);
	}
	int mem;
	int i;
	double x[NVARS+1];

	for (mem = 0; mem < POPSIZE; mem++)
	{
		for (i = 0; i < NVARS; i++)
			x[i+1] = population[mem].gene[i];

		population[mem].fitness = x[1]*x[1]+x[2]*x[2];


		if (population[mem].fitness <40 && population[mem].fitness>35)
		{
			fprintf(output, "\n%5d,      %6.9f, %6.9f, %6.9f ", generation, 
				x[1],x[2],population[mem].fitness);
		}

	}
}

/***************************************************************/
/* Keep_the_best function: This function keeps track of the    */
/* best member of the population. Note that the last entry in  */
/* the array Population holds a copy of the best individual    */
/***************************************************************/

void keep_the_best()
{
	int mem;
	int i;
	cur_best = 0; /* stores the index of the best individual */

	for (mem = 0; mem < POPSIZE; mem++)
	{
		if (population[mem].fitness > population[POPSIZE].fitness)
		{
			cur_best = mem;
			population[POPSIZE].fitness = population[mem].fitness;
		}
	}
	/* once the best member in the population is found, copy the genes */
	for (i = 0; i < NVARS; i++)
		population[POPSIZE].gene[i] = population[cur_best].gene[i];
}

/****************************************************************/
/* Elitist function: The best member of the previous generation */
/* is stored as the last in the array. If the best member of    */
/* the current generation is worse then the best member of the  */
/* previous generation, the latter one would replace the worst  */
/* member of the current population                             */
/****************************************************************/

void elitist()
{
	int i;
	double best, worst;             /* best and worst fitness values */
	int best_mem, worst_mem; /* indexes of the best and worst member */

	best = population[0].fitness;
	worst = population[0].fitness;
	for (i = 0; i < POPSIZE - 1; ++i)
	{
		if(population[i].fitness > population[i+1].fitness)
		{      
			if (population[i].fitness >= best)
			{
				best = population[i].fitness;
				best_mem = i;
			}
			if (population[i+1].fitness <= worst)
			{
				worst = population[i+1].fitness;
				worst_mem = i + 1;
			}
		}
		else
		{
			if (population[i].fitness <= worst)
			{
				worst = population[i].fitness;
				worst_mem = i;
			}
			if (population[i+1].fitness >= best)
			{
				best = population[i+1].fitness;
				best_mem = i + 1;
			}
		}
	}
	/* if best individual from the new population is better than */
	/* the best individual from the previous population, then    */
	/* copy the best from the new population; else replace the   */
	/* worst individual from the current population with the     */
	/* best one from the previous generation                     */

	if (best >= population[POPSIZE].fitness)
	{
		for (i = 0; i < NVARS; i++)
			population[POPSIZE].gene[i] = population[best_mem].gene[i];
		population[POPSIZE].fitness = population[best_mem].fitness;
	}
	else
	{
		for (i = 0; i < NVARS; i++)
			population[worst_mem].gene[i] = population[POPSIZE].gene[i];
		population[worst_mem].fitness = population[POPSIZE].fitness;
	} 
}
/**************************************************************/
/* Selection function: Standard proportional selection for    */
/* maximization problems incorporating elitist model - makes  */
/* sure that the best member survives                         */
/**************************************************************/

void select(void)
{
	int mem, i, j, k;
	double sum = 0;
	double p;

	/* find total fitness of the population */
	for (mem = 0; mem < POPSIZE; mem++)
	{
		sum += population[mem].fitness;
	}

	/* calculate relative fitness */
	for (mem = 0; mem < POPSIZE; mem++)
	{
		population[mem].rfitness =  population[mem].fitness/sum;
	}
	population[0].cfitness = population[0].rfitness;

	/* calculate cumulative fitness */
	for (mem = 1; mem < POPSIZE; mem++)
	{
		population[mem].cfitness =  population[mem-1].cfitness +       
			population[mem].rfitness;
	}

	/* finally select survivors using cumulative fitness. */

	for (i = 0; i < POPSIZE; i++)
	{ 
		p = rand()%1000/1000.0;
		if (p < population[0].cfitness)
			newpopulation[i] = population[0];      
		else
		{
			for (j = 0; j < POPSIZE;j++)      
				if (p >= population[j].cfitness && 
					p<population[j+1].cfitness)
					newpopulation[i] = population[j+1];
		}
	}
	/* once a new population is created, copy it back */

	for (i = 0; i < POPSIZE; i++)
		population[i] = newpopulation[i];      
}

/***************************************************************/
/* Crossover selection: selects two parents that take part in  */
/* the crossover. Implements a single point crossover          */
/***************************************************************/

void crossover(void)
{
	int i, mem, one;
	int first  =  0; /* count of the number of members chosen */
	double x;

	for (mem = 0; mem < POPSIZE; ++mem)
	{
		x = rand()%1000/1000.0;
		if (x < PXOVER)
		{
			++first;
			if (first % 2 == 0)
				Xover(one, mem);
			else
				one = mem;
		}
	}
}
/**************************************************************/
/* Crossover: performs crossover of the two selected parents. */
/**************************************************************/

void Xover(int one, int two)
{
	int i;
	int point; /* crossover point */

	/* select crossover point */
	if(NVARS > 1)
	{
		if(NVARS == 2)
			point = 1;
		else
			point = (rand() % (NVARS - 1)) + 1;

		for (i = 0; i < point; i++)
			swap(&population[one].gene[i], &population[two].gene[i]);

	}
}

/*************************************************************/
/* Swap: A swap procedure that helps in swapping 2 variables */
/*************************************************************/

void swap(double *x, double *y)
{
	double temp;

	temp = *x;
	*x = *y;
	*y = temp;

}

/**************************************************************/
/* Mutation: Random uniform mutation. A variable selected for */
/* mutation is replaced by a random value between lower and   */
/* upper bounds of this variable                              */
/**************************************************************/

void mutate(void)
{
	int i, j;
	double lbound, hbound;
	double x;

	for (i = 0; i < POPSIZE; i++)
		for (j = 0; j < NVARS; j++)
		{
			x = rand()%1000/1000.0;
			if (x < PMUTATION)
			{
				/* find the bounds on the variable to be mutated */
				lbound = population[i].lower[j];
				hbound = population[i].upper[j];  
				population[i].gene[j] = randval(lbound, hbound);
			}
		}
}

/***************************************************************/
/* Report function: Reports progress of the simulation. Data   */
/* dumped into the  output file are separated by commas        */
/***************************************************************/

void report(void)
{
	int i;
	double best_val;            /* best population fitness */
	double avg;                 /* avg population fitness */
	double stddev;              /* std. deviation of population fitness */
	double sum_square;          /* sum of square for std. calc */
	double square_sum;          /* square of sum for std. calc */
	double sum;                 /* total population fitness */

	sum = 0.0;
	sum_square = 0.0;

	for (i = 0; i < POPSIZE; i++)
	{
		sum += population[i].fitness;
		sum_square += population[i].fitness * population[i].fitness;
	}

	avg = sum/(double)POPSIZE;
	square_sum = avg * avg * POPSIZE;
	stddev = sqrt((sum_square - square_sum)/(POPSIZE - 1));
	best_val = population[POPSIZE].fitness;

	fprintf(galog, "\n%5d,      %6.9f, %6.9f, %6.9f ", generation, 
		best_val, avg, stddev);
}


/**************************************************************/
/* Main function: Each generation involves selecting the best */
/* members, performing crossover & mutation and then          */
/* evaluating the resulting population, until the terminating */
/* condition is satisfied                                     */
/**************************************************************/

void main(void)
{
	int i;
	if ((galog = fopen("galog.txt","w"))==NULL)
	{
		exit(1);
	}
	generation = 0;

	fprintf(galog, "\n generation  best  average  standard \n");
	fprintf(galog, " number      value fitness  deviation \n");

	initialize();
	evaluate();
	keep_the_best();
	while(generation<MAXGENS)
	{
		generation++;
		select();
		crossover();
		mutate();
		report();
		evaluate();
		elitist();
	}
	fprintf(galog,"\n\n Simulation completed\n");
	fprintf(galog,"\n Best member: \n");

	for (i = 0; i < NVARS; i++)
	{
		fprintf (galog,"\n var(%d) = %3.9f",i,population[POPSIZE].gene[i]);
	}
	fprintf(galog,"\n\n Best fitness = %3.9f",population[POPSIZE].fitness);
	fclose(galog);
	printf("Success\n");
	getchar();
}
/***************************************************************/