遗传算法函数优化程序（C++）
2011-11-06 20:44
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/******************************************************************
** Sample Genetic Algorithm **
** Version 0.1 **
******************************************************************/
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
/************************************************
** The Definition of Constant **
************************************************/
#define POPSIZE 500 // population size
#define MAXIMIZATION 1 // maximization flag
#define MINIMIZATION 2 // minimization flag
/**************************************************
** The Definition of User Data **
**(For different problem,there are somedifferent**
**************************************************/
#define Cmax 100 // certain maximal value
#define Cmin 0 // certain minimum value
#define LENGTH1 10 // the chromosome length of 1stvariable
#define LENGTH2 10 // the chromosome length of 2ndvariable
#define CHROMLENGTH LENGTH1+LENGTH2 // total length of chromosome
int FunctionMode = MAXIMIZATION; // optimization type
int PopSize = 80; // population size
int MaxGeneration = 200; // max. number of generation
double Pc = 0.6; // probability of crossover
double Pm = 0.001; // probability of mutation
/************************************************
** The Definition of Data Structure **
************************************************/
struct individual
{
charchrom[CHROMLENGTH+1]; // astring of code representing individual
doublevalue; // objectvalue of this individual
doublefitness; //fitness value of this individual
};
/************************************************
** The Definition of Globle Variables **
************************************************/
int generation; // number ofgeneration
int best_index; // index of bestindividual
int worst_index; // index of wrostindividual
struct individual bestindividual; // best individual of current generation
struct individual worstindividual; // wrost individual of current generation
struct individual currentbest; // best individual by now
struct individual population[POPSIZE]; // population
/************************************************
** Declaration of Prototype **
************************************************/
void GenerateInitialPopulation(void);
void GenerateNextPopulation(void);
void EvaluatePopulation(void);
long DecodeChromosome(char*, int, int);
void CalculateObjectValue(void);
void CalculateFitnessValue(void);
void FindBestAndWorstIndividual(void);
void PerformEvolution(void);
void SelectionOperator(void);
void CrossoverOperator(void);
void MutationOperator(void);
void OutputTextReport(void);
/************************************************
** main program **
************************************************/
void main(void)
{
generation=0;
GenerateInitialPopulation();
EvaluatePopulation();
while(generation < MaxGeneration)
{
generation++;
GenerateNextPopulation();
EvaluatePopulation();
PerformEvolution();
OutputTextReport();
}
}
/************************************************
** Functino: Generate the first population **
** Variables: None **
************************************************/
void GenerateInitialPopulation(void)
{
inti, j;
//randomize();
for(i=0; i<PopSize; i++)
{
for(j=0; j<CHROMLENGTH; j++)
population[i].chrom[j]=(rand()%10<5)?'0':'1';
}
population[i].chrom[CHROMLENGTH]='\0';
}
/************************************************
** Functino: Initial the first generation **
** Variables: None **
************************************************/
void GenerateNextPopulation(void)
{
SelectionOperator();
CrossoverOperator();
MutationOperator();
}
/*****************************************************************
** Functino: Evaluate population accordingto certain formula **
** Variables: None **
*****************************************************************/
void EvaluatePopulation(void)
{
CalculateObjectValue();
CalculateFitnessValue();
FindBestAndWorstIndividual();
}
/************************************************
** Functino:To decode a binary chromosome **
** into a decimal integer **
** Variables: None **
** Note: The returned value may be plus, or **
** minus. For different coding method, **
** this value may be changed into **
** "unsigned int". **
************************************************/
long DecodeChromosome(char* string, intpoint, int length)
{
inti;
longdecimal = 0L;
char*pointer;
for(i=0, pointer=string+point; i<length; i++, pointer++)
{
decimal+= (*pointer-'0')<<(length-1-i);
}
return(decimal);
}
/************************************************
** Functino: To calculate object value **
** Variables: None **
** Note: For different problem, user must **
** change these code. This example is **
** dealing with Rosenbrock function. **
** f(x1,x2)=100*(x1^2-x2)^2+(1-x1)^2 **
** Its maximal value is: **
** f(-2.048, -2.048) = 3905.926227 **
************************************************/
void CalculateObjectValue(void)
{
inti;
longtemp1, temp2;
doublex1, x2;
//Rosenbrock function
for(i=0; i<PopSize; i++)
{
temp1=DecodeChromosome(population[i].chrom,0, LENGTH1);
temp2=DecodeChromosome(population[i].chrom,LENGTH1,LENGTH2);
x1=4.096*temp1/1023.0-2.048;
x2=4.096*temp2/1023.0-2.048;
population[i].value=100*(x1*x1-x2)*(x1*x1-x2)+(1-x1)*(1-x1);
}
}
/************************************************
** Functino: To calculate the fitness value **
** Variables: None **
************************************************/
void CalculateFitnessValue(void)
{
inti;
doubletemp;
for(i=0; i<PopSize; i++)
{
if(FunctionMode == MAXIMIZATION)
{
if((population[i].value+Cmin)>0.0)
{
temp=Cmin+population[i].value;
}else
{
temp=0.0;
}
}elseif (FunctionMode == MINIMIZATION)
{
if(population[i].value<Cmax)
{
temp=Cmax-population[i].value;
}else
{
temp= 0.0;
}
}
population[i].fitness=temp;
}
}
/************************************************
** Functino: To find out the best individual **
** so far current generation. **
** Variables: None **
************************************************/
void FindBestAndWorstIndividual(void)
{
inti;
doublesum=0.0;
//find out the best and wrost individual of this generation
bestindividual=population[0];
worstindividual=population[0];
for(i=1; i<PopSize; i++)
{
if(population[i].fitness > bestindividual.fitness)
{
bestindividual=population[i];
best_index=i;
}elseif (population[i].fitness < worstindividual.fitness)
{
worstindividual=population[i];
worst_index=i;
}
sum+= population[i].fitness;
}
//find out the best individual so far
if(generation == 0) //initialize the best individual
{
currentbest= bestindividual;
}elseif (bestindividual.fitness>currentbest.fitness)
{
currentbest= bestindividual;
}
}
/************************************************
** Functino: To perform evolution operation **
** based on elitise model. Elitist **
** model is to replace theworst **
** individual of this generation **
** by the current best one. **
** Variables: None **
************************************************/
void PerformEvolution(void)
{
if(bestindividual.fitness > currentbest.fitness)
{
currentbest= population[best_index];
}else
population[worst_index]= currentbest;
}
/************************************************
** Functino: To reproduce a chromosome by **
** proportional selection. **
** Variables: None **
************************************************/
void SelectionOperator(void)
{
inti, index;
doublep, sum=0.0;
doublecfitness[POPSIZE]; // cumulative fitness value
structindividual newpopulation[POPSIZE];
//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++)
{
/*Seed the random-number generator with current time so that
* the numbers will be different every time werun.
*/
//srand((unsigned)time(NULL) );
p=rand()%1000/1000.0;
index=0;
while(p>cfitness[index])
index++;
newpopulation[i]= population[index];
}
for(i=0; i< PopSize; i++)
population[i]= newpopulation[i];
}
/************************************************
** Functino: Crossover two chromosome by **
** means of one-point crossover **
** Variables: None **
************************************************/
void CrossoverOperator(void)
{
inti,j;
intindex[POPSIZE];
intpoint, temp;
doublep;
char ch;
//make a pair of individual randomly
for(i=0; i<PopSize; i++)
index[i]=i;
for(i=0; i<PopSize; i++)
{
point= rand()%(PopSize-i);
temp= index[i];
index[i]= index[point+i];
index[point+i]= temp;
}
//one-point crossover operation
for(i=0; i<PopSize-1; i+=2)
{
p= rand()%1000/1000.0;
if(p<Pc)
{
//point= random(CHROMLENGTH-1)+1;
point= rand()%(CHROMLENGTH-1)+1;
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;
}
}
}
}
/************************************************
** Functino: Mutation of a chromosome. **
** Variables: None **
************************************************/
void MutationOperator(void)
{
inti, j;
doublep;
//bit mutation;
for(i=0; i<PopSize; i++)
{
for(j=0; j<CHROMLENGTH; j++)
{
p= rand()%1000/1000.0;
if(p<Pm)
population[i].chrom[j]=(population[i].chrom[j]=='0')?'1':'0';
}
}
}
/************************************************
** Functino: Output the results of current **
** population. **
** Variables: None **
************************************************/
void OutputTextReport(void)
{
inti;
doublesum; // temporary sum
doubleaverage; // average ofpopulation object value
//calculate average object value
sum= 0.0;
for(i=0; i<PopSize; i++)
sum+= population[i].value;
average= sum/PopSize;
//print results of this population
printf("gen=%d,avg+%f, best=%f,", generation, average, currentbest.value);
printf("chromosome=");
for(i=0; i<CHROMLENGTH; i++)
printf("%c",currentbest.chrom[i]);
printf("\n");
}
** Sample Genetic Algorithm **
** Version 0.1 **
******************************************************************/
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
/************************************************
** The Definition of Constant **
************************************************/
#define POPSIZE 500 // population size
#define MAXIMIZATION 1 // maximization flag
#define MINIMIZATION 2 // minimization flag
/**************************************************
** The Definition of User Data **
**(For different problem,there are somedifferent**
**************************************************/
#define Cmax 100 // certain maximal value
#define Cmin 0 // certain minimum value
#define LENGTH1 10 // the chromosome length of 1stvariable
#define LENGTH2 10 // the chromosome length of 2ndvariable
#define CHROMLENGTH LENGTH1+LENGTH2 // total length of chromosome
int FunctionMode = MAXIMIZATION; // optimization type
int PopSize = 80; // population size
int MaxGeneration = 200; // max. number of generation
double Pc = 0.6; // probability of crossover
double Pm = 0.001; // probability of mutation
/************************************************
** The Definition of Data Structure **
************************************************/
struct individual
{
charchrom[CHROMLENGTH+1]; // astring of code representing individual
doublevalue; // objectvalue of this individual
doublefitness; //fitness value of this individual
};
/************************************************
** The Definition of Globle Variables **
************************************************/
int generation; // number ofgeneration
int best_index; // index of bestindividual
int worst_index; // index of wrostindividual
struct individual bestindividual; // best individual of current generation
struct individual worstindividual; // wrost individual of current generation
struct individual currentbest; // best individual by now
struct individual population[POPSIZE]; // population
/************************************************
** Declaration of Prototype **
************************************************/
void GenerateInitialPopulation(void);
void GenerateNextPopulation(void);
void EvaluatePopulation(void);
long DecodeChromosome(char*, int, int);
void CalculateObjectValue(void);
void CalculateFitnessValue(void);
void FindBestAndWorstIndividual(void);
void PerformEvolution(void);
void SelectionOperator(void);
void CrossoverOperator(void);
void MutationOperator(void);
void OutputTextReport(void);
/************************************************
** main program **
************************************************/
void main(void)
{
generation=0;
GenerateInitialPopulation();
EvaluatePopulation();
while(generation < MaxGeneration)
{
generation++;
GenerateNextPopulation();
EvaluatePopulation();
PerformEvolution();
OutputTextReport();
}
}
/************************************************
** Functino: Generate the first population **
** Variables: None **
************************************************/
void GenerateInitialPopulation(void)
{
inti, j;
//randomize();
for(i=0; i<PopSize; i++)
{
for(j=0; j<CHROMLENGTH; j++)
population[i].chrom[j]=(rand()%10<5)?'0':'1';
}
population[i].chrom[CHROMLENGTH]='\0';
}
/************************************************
** Functino: Initial the first generation **
** Variables: None **
************************************************/
void GenerateNextPopulation(void)
{
SelectionOperator();
CrossoverOperator();
MutationOperator();
}
/*****************************************************************
** Functino: Evaluate population accordingto certain formula **
** Variables: None **
*****************************************************************/
void EvaluatePopulation(void)
{
CalculateObjectValue();
CalculateFitnessValue();
FindBestAndWorstIndividual();
}
/************************************************
** Functino:To decode a binary chromosome **
** into a decimal integer **
** Variables: None **
** Note: The returned value may be plus, or **
** minus. For different coding method, **
** this value may be changed into **
** "unsigned int". **
************************************************/
long DecodeChromosome(char* string, intpoint, int length)
{
inti;
longdecimal = 0L;
char*pointer;
for(i=0, pointer=string+point; i<length; i++, pointer++)
{
decimal+= (*pointer-'0')<<(length-1-i);
}
return(decimal);
}
/************************************************
** Functino: To calculate object value **
** Variables: None **
** Note: For different problem, user must **
** change these code. This example is **
** dealing with Rosenbrock function. **
** f(x1,x2)=100*(x1^2-x2)^2+(1-x1)^2 **
** Its maximal value is: **
** f(-2.048, -2.048) = 3905.926227 **
************************************************/
void CalculateObjectValue(void)
{
inti;
longtemp1, temp2;
doublex1, x2;
//Rosenbrock function
for(i=0; i<PopSize; i++)
{
temp1=DecodeChromosome(population[i].chrom,0, LENGTH1);
temp2=DecodeChromosome(population[i].chrom,LENGTH1,LENGTH2);
x1=4.096*temp1/1023.0-2.048;
x2=4.096*temp2/1023.0-2.048;
population[i].value=100*(x1*x1-x2)*(x1*x1-x2)+(1-x1)*(1-x1);
}
}
/************************************************
** Functino: To calculate the fitness value **
** Variables: None **
************************************************/
void CalculateFitnessValue(void)
{
inti;
doubletemp;
for(i=0; i<PopSize; i++)
{
if(FunctionMode == MAXIMIZATION)
{
if((population[i].value+Cmin)>0.0)
{
temp=Cmin+population[i].value;
}else
{
temp=0.0;
}
}elseif (FunctionMode == MINIMIZATION)
{
if(population[i].value<Cmax)
{
temp=Cmax-population[i].value;
}else
{
temp= 0.0;
}
}
population[i].fitness=temp;
}
}
/************************************************
** Functino: To find out the best individual **
** so far current generation. **
** Variables: None **
************************************************/
void FindBestAndWorstIndividual(void)
{
inti;
doublesum=0.0;
//find out the best and wrost individual of this generation
bestindividual=population[0];
worstindividual=population[0];
for(i=1; i<PopSize; i++)
{
if(population[i].fitness > bestindividual.fitness)
{
bestindividual=population[i];
best_index=i;
}elseif (population[i].fitness < worstindividual.fitness)
{
worstindividual=population[i];
worst_index=i;
}
sum+= population[i].fitness;
}
//find out the best individual so far
if(generation == 0) //initialize the best individual
{
currentbest= bestindividual;
}elseif (bestindividual.fitness>currentbest.fitness)
{
currentbest= bestindividual;
}
}
/************************************************
** Functino: To perform evolution operation **
** based on elitise model. Elitist **
** model is to replace theworst **
** individual of this generation **
** by the current best one. **
** Variables: None **
************************************************/
void PerformEvolution(void)
{
if(bestindividual.fitness > currentbest.fitness)
{
currentbest= population[best_index];
}else
population[worst_index]= currentbest;
}
/************************************************
** Functino: To reproduce a chromosome by **
** proportional selection. **
** Variables: None **
************************************************/
void SelectionOperator(void)
{
inti, index;
doublep, sum=0.0;
doublecfitness[POPSIZE]; // cumulative fitness value
structindividual newpopulation[POPSIZE];
//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++)
{
/*Seed the random-number generator with current time so that
* the numbers will be different every time werun.
*/
//srand((unsigned)time(NULL) );
p=rand()%1000/1000.0;
index=0;
while(p>cfitness[index])
index++;
newpopulation[i]= population[index];
}
for(i=0; i< PopSize; i++)
population[i]= newpopulation[i];
}
/************************************************
** Functino: Crossover two chromosome by **
** means of one-point crossover **
** Variables: None **
************************************************/
void CrossoverOperator(void)
{
inti,j;
intindex[POPSIZE];
intpoint, temp;
doublep;
char ch;
//make a pair of individual randomly
for(i=0; i<PopSize; i++)
index[i]=i;
for(i=0; i<PopSize; i++)
{
point= rand()%(PopSize-i);
temp= index[i];
index[i]= index[point+i];
index[point+i]= temp;
}
//one-point crossover operation
for(i=0; i<PopSize-1; i+=2)
{
p= rand()%1000/1000.0;
if(p<Pc)
{
//point= random(CHROMLENGTH-1)+1;
point= rand()%(CHROMLENGTH-1)+1;
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;
}
}
}
}
/************************************************
** Functino: Mutation of a chromosome. **
** Variables: None **
************************************************/
void MutationOperator(void)
{
inti, j;
doublep;
//bit mutation;
for(i=0; i<PopSize; i++)
{
for(j=0; j<CHROMLENGTH; j++)
{
p= rand()%1000/1000.0;
if(p<Pm)
population[i].chrom[j]=(population[i].chrom[j]=='0')?'1':'0';
}
}
}
/************************************************
** Functino: Output the results of current **
** population. **
** Variables: None **
************************************************/
void OutputTextReport(void)
{
inti;
doublesum; // temporary sum
doubleaverage; // average ofpopulation object value
//calculate average object value
sum= 0.0;
for(i=0; i<PopSize; i++)
sum+= population[i].value;
average= sum/PopSize;
//print results of this population
printf("gen=%d,avg+%f, best=%f,", generation, average, currentbest.value);
printf("chromosome=");
for(i=0; i<CHROMLENGTH; i++)
printf("%c",currentbest.chrom[i]);
printf("\n");
}
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