// ---------------------------------------------------------------------------
// Just loop through all the individuals
void EvaluateAll( GPEnvironment& environment )
{
int n_individuals = environment.GetPopulationSize();
for( int i = 0; i < n_individuals; ++i )
{
// for each dog, we'll run the test a number of times
// where each time a stick will be thrown, and the dog
// will need to fetch it.
// its fitness will be graded as an average of these attempts.
GPFitness individual_fitness = 0;
const int kThrows = 10;
for( int j = 0; j < kThrows; ++j )
{
individual_fitness += EvaluateIndividual( environment, i );
}
individual_fitness = individual_fitness / kThrows;
// after putting the dog through its paces, we will manually call the fitness
// function to see how well it did. we can then override the fitness value
// which the framework has for it.
// NOTE: the framework needs to have either automatically called the Fitness
// or otherwise had it set with an OverrideIndividualFitness() call
// because it requires the fitness value to perform crossover and mutations.
environment.OverrideIndividualFitness( i, individual_fitness );
}
}
void DE(Individual Pop, int n, Individual New)
{
int i, best;
int p1, p2, p3;
if(Gen==1) {
DEBest=0;
for(i=1; i<n; i++)
if(Better(&Pop[i], &Pop[DEBest])) DEBest=i;
}
best=DEBest;
for(i=0; i<n; i++) {
DEselect(p1, p2, p3);
if(DEBinomialCrossoverFlag)
DE1bin(New[i].x, Pop[i].x,
Pop[p1].x, Pop[p2].x, Pop[p3].x)
else
DE1exp(New[i].x, Pop[i].x,
Pop[p1].x, Pop[p2].x, Pop[p3].x)
EvaluateIndividual(&New[i]);
if(Better(&New[i], &Pop[i])) { /* new is better than old */
if(Better(&New[i], &Pop[DEBest])) {
if(!SwapPopulationFlag) DEBest=i;
else best=i;
}
if(!SwapPopulationFlag)
Copy(&Pop[i], &New[i]);
SuccessEval++;
}
else {
if(SwapPopulationFlag)
Copy(&New[i], &Pop[i]);
FailEval++;
}
}
if(SwapPopulationFlag) DEBest=best;
}