Back to Lesson 1 - Tutorial main page - Algorithm-Based - Component-Based page - Programming hints - EO documentation

Code for FirstRealGA

Click on the figure to see the corresponding code.
In the code, the colors are meaningfull
The actual code is in boldface and the comment in normal face.

 
//-----------------------------------------------------------------------------
// FirstRealGA.cpp
//-----------------------------------------------------------------------------
//*
// An instance of a VERY simple Real-coded Genetic Algorithm
//
//-----------------------------------------------------------------------------
// standard includes
#include <stdexcept>  // runtime_error
#include <iostream>    // cout
#include <strstream>  // ostrstream, istrstream
// the general include for eo
#include <eo>
#include <es.h>
//-----------------------------------------------------------------------------
// define your individuals
typedef eoReal<double> Indi;
//-----------------------------------------------------------------------------
// a simple fitness function that computes the euclidian norm of a real vector
//      @param _indi A real-valued individual
double real_value(const Indi & _indi)
{
 double sum = 0;
 for (unsigned i = 0; i < _indi.size(); i++)
         sum += _indi[i]*_indi[i];
 return (-sum);                      // maximizing only
}
//-----------------------------------------------------------------------------
void main_function(int argc, char **argv)
{
 // all parameters are hard-coded!
 const unsigned int SEED = 42; // seed for random number generator
 const unsigned int VEC_SIZE = 8; // Number of object variables in genotypes
 const unsigned int POP_SIZE = 20; // Size of population
 const unsigned int T_SIZE = 3; // size for tournament selection
 const unsigned int MAX_GEN = 500; // Maximum number of generation before STOP
 const float CROSS_RATE = 0.8; // Crossover rate
 const double EPSILON = 0.01;  // range for real uniform mutation
 const float MUT_RATE = 0.5;    // mutation rate
 //////////////////////////
 //  Random seed
 //////////////////////////
 //reproducible random seed: if you don't change SEED above,
 // you'll aways get the same result, NOT a random run
 rng.reseed(SEED);
 /////////////////////////////
 // Fitness function
 ////////////////////////////
 // Evaluation: from a plain C++ fn to an EvalFunc Object
 eoEvalFuncPtr<Indi> eval(  real_value );
 ////////////////////////////////
 // Initilisation of population
 ////////////////////////////////
 // declare the population
 eoPop<Indi> pop;
 // fill it!
 for (unsigned int igeno=0; igeno<POP_SIZE; igeno++)
     {
         Indi v;                  // void individual, to be filled
         for (unsigned ivar=0; ivar<VEC_SIZE; ivar++)
             {
                 double r = 2*rng.uniform() - 1; // new value, random in [-1,1)
                 v.push_back(r);            // append that random value to v
             }
         eval(v);                                  // evaluate it
         pop.push_back(v);                // and put it in the population
     }
 // sort pop before printing it!
 pop.sort();
 // Print (sorted) intial population (raw printout)
 cout << "Initial Population" << endl;
 cout << pop;
 /////////////////////////////////////
 // selection and replacement
 ////////////////////////////////////
 // The robust tournament selection
 eoDetTournamentSelect<Indi> select(T_SIZE);            // T_SIZE in [2,POP_SIZE]
 // eoSGA uses generational replacement by default
 // so no replacement procedure has to be given
 //////////////////////////////////////
 // termination condition
 /////////////////////////////////////
 // stop after MAX_GEN generations
 eoGenContinue<Indi> continuator(MAX_GEN);
 
 //////////////////////////////////////
 // The variation operators
 //////////////////////////////////////
 // offspring(i) uniformly chosen in [parent(i)-epsilon, parent(i)+epsilon]
 eoUniformMutation<Indi>  mutation(EPSILON);
 // offspring(i) is a linear combination of parent(i)
 eoSegmentCrossover<Indi> xover;
 /////////////////////////////////////////
 // the algorithm
 ////////////////////////////////////////
 // standard Generational GA requires
 // selection, evaluation, crossover and mutation, stopping criterion

 eoSGA<Indi> gga(select, xover, CROSS_RATE, mutation, MUT_RATE,
                                   eval, continuator);
 // Apply algo to pop - that's it!
 gga(pop);
 
 // Print (sorted) intial population
 pop.sort();
 cout << "FINAL Population\n" << pop << endl;
}
// A main that catches the exceptions
int main(int argc, char **argv)
{
#ifdef _MSC_VER
 //  rng.reseed(42);
     int flag = _CrtSetDbgFlag(_CRTDBG_LEAK_CHECK_DF);
       flag |= _CRTDBG_LEAK_CHECK_DF;
     _CrtSetDbgFlag(flag);
//    _CrtSetBreakAlloc(100);
#endif
     try
     {
             main_function(argc, argv);
     }
     catch(exception& e)
     {
             cout << "Exception: " << e.what() << '\n';
     }
     return 1;
}
Back to Lesson 1 - Tutorial main page - Algorithm-Based - Component-Based page - Programming hints - EO documentation
Marc Schoenauer

Last modified: Sun Nov 19 08:31:29 2000