main( int argc, char** argv) {
unsigned numGenes = 2, // Number of genes in the chromosome
numGenerations, // Number of generations
popSize; // Population size
float xOverPr, // Crossover rate, from 0 to 1
mutPr; // Mutation rate, from 0 to 1
bool verbose; // true if you want many things printed
// Obtain command line parameters or default values
getParams( argc, argv, verbose, popSize, numGenerations,
xOverPr, mutPr );
// Create an initial population and the population-level operators
Pop pop;
unsigned i, j;
Uniform<float> u( 0, 1);
for ( j = 0; j < popSize; j ++ ) {
Chrom* aChrom = new Chrom;
for ( i = 0; i < numGenes; i ++ ) {
aChrom->push_back( u() );
}
pop.push_back( *aChrom );
if ( verbose ) {
copy( aChrom->begin(), aChrom->end(), ostream_iterator<int>( cout, " ") );
cout << endl;
}
delete aChrom;
}
// Create an instance of the evaluation function object
deJongF2float thisEvalFunc;
// From that, create an object that evaluates all the population
EOEvalAll<Chrom> eval( thisEvalFunc) ;
// Then, an object that select who's going to reproduce
EOLottery<Chrom> select;
// Another object that mates them
EORandomBreed<Chrom> breed;
// And another that weeds out the worst
EOElimAll<Chrom> replace;
// This one check if the algorithm has finishd or not
EOGenTerm<Chrom> term( numGenerations );
// Then the EO-level operators
// This is a mutation-like operator
EOFloatArrayRndMutate<Chrom> rndMut( 0.1, mutPr );
// And this is the classical 2-point crossover
EOXOver2<Chrom> xOver(xOverPr);
// Add operators to breeder
breed.addOp( &rndMut );
breed.addOp( &xOver );
// Then, the algorithm itself is instantiated
EOEasyGA<Chrom> ga( eval, select, breed, term, replace, verbose );
// Now, apply the algorithm to the population
eval( pop );
ga( pop );
// Sort Population. First is the best
sort( pop.begin(), pop.end(), SortEO<Chrom>() );
// And print results!
cout << "Best fitness ....... " << pop[0].fitness() << endl
<< "Value............... " << pop[0] << endl;
return 0;
}
