ga_tutorial.cpp

簡單的基因演算法

//-----------------------------------------ga_tutorial.cpp--------------------------------------
//
//	code to illustrate the use of a genetic algorithm to solve the problem described
//  at www.ai-junkie.com
//
//	by Mat Buckland aka fup
//
//-----------------------------------------------------------------------------------------------
#include <string>
#include <stdlib.h>
#include <iostream.h>
#include <time.h>
#include <math.h>

using std::string;

#define CROSSOVER_RATE            0.75			//0.7
#define MUTATION_RATE             0.001			//0.001
#define POP_SIZE                  100			//100_must be an even number
#define CHROMO_LENGTH             300			//300
#define GENE_LENGTH               4				//4
#define MAX_ALLOWABLE_GENERATIONS	400			//400

//returns a float between 0 & 1
#define RANDOM_NUM		((float)rand()/(RAND_MAX+1))

//----------------------------------------------------------------------------------------
//
//	define a data structure which will define a chromosome
//
//----------------------------------------------------------------------------------------
struct chromo_typ
{
	//the binary bit string is held in a std::string
  string	bits;  

	float	  fitness;

	chromo_typ(): bits(""), fitness(0.0f){};
	chromo_typ(string bts, float ftns): bits(bts), fitness(ftns){}
};


/////////////////////////////////prototypes/////////////////////////////////////////////////////

void    PrintGeneSymbol(int val);
string  GetRandomBits(int length);
int     BinToDec(string bits);
float   AssignFitness(string bits, int target_value);
void    PrintChromo(string bits);
void    PrintGeneSymbol(int val);
int     ParseBits(string bits, int* buffer);
string  Roulette(int total_fitness, chromo_typ* Population);
void    Mutate(string &bits);
void    Crossover(string &offspring1, string &offspring2);



//-------------------------------main--------------------------------------------------
//
//-------------------------------------------------------------------------------------
int main()
{
	//seed the random number generator
	srand((int)time(NULL));

  //just loop endlessly until user gets bored :0)
  while (true)
  {
    //storage for our population of chromosomes.
    chromo_typ Population[POP_SIZE];

	  //get a target number from the user. (no error checking)
	  float Target;
	  
	  cin >> Target;
    cout << endl << endl;
	  
	  //first create a random population, all with zero fitness.
	  for (int i=0; i<POP_SIZE; i++)
	  {
		  Population[i].bits	  = GetRandomBits(CHROMO_LENGTH);
		  Population[i].fitness = 0.0f;
	  }

	  int GenerationsRequiredToFindASolution = 0;

	  //we will set this flag if a solution has been found
	  bool bFound = false;

	  //enter the main GA loop
	  while(!bFound)
	  {
		  //this is used during roulette wheel sampling
		  float TotalFitness = 0.0f;

		  // test and update the fitness of every chromosome in the 
      // population
		  for (int i=0; i<POP_SIZE; i++)
		  {
			  Population[i].fitness = AssignFitness(Population[i].bits, Target);

			  TotalFitness += Population[i].fitness;
		  }

		  // check to see if we have found any solutions (fitness will be 999)
		  for (i=0; i<POP_SIZE; i++)
		  {
			  if (Population[i].fitness == 999.0f)
			  {
          cout << "\nSolution found in " << GenerationsRequiredToFindASolution << " generations!" << endl << endl;;

				  PrintChromo(Population[i].bits);

				  bFound = true;

          break;
			  }
		  }

		  // create a new population by selecting two parents at a time and creating offspring
      // by applying crossover and mutation. Do this until the desired number of offspring
      // have been created. 
		  
		  //define some temporary storage for the new population we are about to create
		  chromo_typ temp[POP_SIZE];

		  int cPop = 0;
	  
		  //loop until we have created POP_SIZE new chromosomes
		  while (cPop < POP_SIZE)
		  {
			  // we are going to create the new population by grabbing members of the old population
			  // two at a time via roulette wheel selection.
			  string offspring1 = Roulette(TotalFitness, Population);
			  string offspring2 = Roulette(TotalFitness, Population);

        //add crossover dependent on the crossover rate
        Crossover(offspring1, offspring2);

			  //now mutate dependent on the mutation rate
			  Mutate(offspring1);
			  Mutate(offspring2);

			  //add these offspring to the new population. (assigning zero as their
        //fitness scores)
			  temp[cPop++] = chromo_typ(offspring1, 0.0f);
			  temp[cPop++] = chromo_typ(offspring2, 0.0f);

		  }//end loop

		  //copy temp population into main population array
		  for (i=0; i<POP_SIZE; i++)
      {
			  Population[i] = temp[i];
      }

		  ++GenerationsRequiredToFindASolution;

		  // exit app if no solution found within the maximum allowable number
		  // of generations
		  if (GenerationsRequiredToFindASolution > MAX_ALLOWABLE_GENERATIONS)
		  {
			  cout << "No solutions found this run!";

			  bFound = true;
		  }

	  }

    cout << "\n\n\n";

  }//end while

	return 0;
}
		



//---------------------------------GetRandomBits-----------------------------------------
//
//	This function returns a string of random 1s and 0s of the desired length.
//
//-----------------------------------------------------------------------------------------
string	GetRandomBits(int length)
{
	string bits;

	for (int i=0; i<length; i++)
	{
		if (RANDOM_NUM > 0.5f)

			bits += "1";

		else

			bits += "0";
	}

	return bits;
}

//---------------------------------BinToDec-----------------------------------------
//
//	converts a binary string into a decimal integer
//
//-----------------------------------------------------------------------------------
int	BinToDec(string bits)
{
	int val			 = 0;
	int value_to_add = 1;

	for (int i = bits.length(); i > 0; i--)
	{
		

		if (bits.at(i-1) == '1')

			val += value_to_add;

		value_to_add *= 2;
	
	}//next bit

	return val;
}


//---------------------------------ParseBits------------------------------------------
//
// Given a chromosome this function will step through the genes one at a time and insert 
// the decimal values of each gene (which follow the operator -> number -> operator rule)
// into a buffer. Returns the number of elements in the buffer.
//------------------------------------------------------------------------------------
int ParseBits(string bits, int* buffer)
{
	
	//counter for buffer position
	int cBuff = 0;
	
	// step through bits a gene at a time until end and store decimal values
	// of valid operators and numbers. Don't forget we are looking for operator - 
	// number - operator - number and so on... We ignore the unused genes 1111
	// and 1110
	
	//flag to determine if we are looking for an operator or a number
	bool bOperator = true;
	
	//storage for decimal value of currently tested gene
	int this_gene = 0;
	
	for (int i=0; i<CHROMO_LENGTH; i+=GENE_LENGTH)
	{
		//convert the current gene to decimal
		this_gene = BinToDec(bits.substr(i, GENE_LENGTH));
		
		//find a gene which represents an operator
		if (bOperator)
		{
			if ( (this_gene < 10) || (this_gene > 13) ) 
				
				continue;
			
			else
			{
				bOperator		= false;
				buffer[cBuff++] = this_gene;
				continue;
			}
		}
		
		//find a gene which represents a number
		else
		{
			if (this_gene > 9)
				
				continue;
			
			else
			{
				bOperator		= true;
				buffer[cBuff++] = this_gene;
				continue;
			}
		}
		
	}//next gene

	//	now we have to run through buffer to see if a possible divide by zero
	//	is included and delete it. (ie a '/' followed by a '0'). We take an easy
	//	way out here and just change the '/' to a '+'. This will not effect the 
	//	evolution of the solution
	for (i=0; i<cBuff; i++)
	{
		if ( (buffer[i] == 13) && (buffer[i+1] == 0) )
		
			buffer[i] = 10;
	}

	return cBuff;
}
	
//---------------------------------AssignFitness--------------------------------------
//
//	given a string of bits and a target value this function will calculate its  
//  representation and return a fitness score accordingly
//------------------------------------------------------------------------------------
float AssignFitness(string bits, int target_value)
{

	//holds decimal values of gene sequence
	int buffer[(int)(CHROMO_LENGTH / GENE_LENGTH)];

	int num_elements = ParseBits(bits, buffer);
	
	// ok, we have a buffer filled with valid values of: operator - number - operator - number..
	// now we calculate what this represents.
	float result = 0.0f;

	for (int i=0; i < num_elements-1; i+=2)
	{
		switch (buffer[i])
		{
			case 10:

				result += buffer[i+1];
				break;

			case 11:
				
				result -= buffer[i+1];
				break;

			case 12:

				result *= buffer[i+1];
				break;

			case 13:
	
				result /= buffer[i+1];
				break;

		}//end switch

	}

	// Now we calculate the fitness. First check to see if a solution has been found
	// and assign an arbitarily high fitness score if this is so.

	if (result == (float)target_value)

		return 999.0f;

	else

		return 1/(float)fabs((double)(target_value - result));
	//	return result;
}

//---------------------------------PrintChromo---------------------------------------
//
// decodes and prints a chromo to screen
//-----------------------------------------------------------------------------------
void PrintChromo(string bits)
{	
	//holds decimal values of gene sequence
	int buffer[(int)(CHROMO_LENGTH / GENE_LENGTH)];
	
	//parse the bit string
	int num_elements = ParseBits(bits, buffer);
	
	for (int i=0; i<num_elements; i++)
  {
		PrintGeneSymbol(buffer[i]);
  }

	return;
}
	
//--------------------------------------PrintGeneSymbol-----------------------------
//	
//	given an integer this function outputs its symbol to the screen 
//----------------------------------------------------------------------------------
void PrintGeneSymbol(int val)
{
	if (val < 10 )
		
		cout << val << " ";
	
	else
	{
		switch (val)
		{
			
		case 10:
			
			cout << "+";
			break;
			
		case 11:
			
			cout << "-";
			break;
			
		case 12:
			
			cout << "*";
			break;
			
		case 13:
			
			cout << "/";
			break;
			
		}//end switch
		
		cout << " ";
	}

	return;
}

//------------------------------------Mutate---------------------------------------
//
//	Mutates a chromosome's bits dependent on the MUTATION_RATE
//-------------------------------------------------------------------------------------
void Mutate(string &bits)
{
	for (int i=0; i<bits.length(); i++)
	{
		if (RANDOM_NUM < MUTATION_RATE)
		{
			if (bits.at(i) == '1')

				bits.at(i) = '0';

			else

				bits.at(i) = '1';
		}
	}

	return;
}

//---------------------------------- Crossover ---------------------------------------
//
//  Dependent on the CROSSOVER_RATE this function selects a random point along the 
//  lenghth of the chromosomes and swaps all the  bits after that point.
//------------------------------------------------------------------------------------
void Crossover(string &offspring1, string &offspring2)
{
  //dependent on the crossover rate
  if (RANDOM_NUM < CROSSOVER_RATE)
  {
    //create a random crossover point
    int crossover = (int) (RANDOM_NUM * CHROMO_LENGTH);

    string t1 = offspring1.substr(0, crossover) + offspring2.substr(crossover, CHROMO_LENGTH);
    string t2 = offspring2.substr(0, crossover) + offspring1.substr(crossover, CHROMO_LENGTH);

    offspring1 = t1; offspring2 = t2;				  
  }
}


//--------------------------------Roulette-------------------------------------------
//
//	selects a chromosome from the population via roulette wheel selection
//------------------------------------------------------------------------------------
string Roulette(int total_fitness, chromo_typ* Population)
{
	//generate a random number between 0 & total fitness count
	float Slice = (float)(RANDOM_NUM * total_fitness);
	
	//go through the chromosones adding up the fitness so far
	float FitnessSoFar = 0.0f;
	
	for (int i=0; i<POP_SIZE; i++)
	{
		FitnessSoFar += Population[i].fitness;
		
		//if the fitness so far > random number return the chromo at this point
		if (FitnessSoFar >= Slice)

			return Population[i].bits;
	}

	return "";
}