void main()
{
int i, test = 0;
// set from global variable
int generation = m;
Individual best;
randomize();
worldInit();
populationInit(1); // 100% of population
fitness(); // explicit, not in populationInit()
best = getBestIndividual();
for(i = 0; i < maxGenerations; i++){
if(test){
printf("#generation: %4d, fitness: %4d\n", i, best.fitness);
}else{
printf("#generation: %4d, fitness: %4d\n", i, best.fitness);
printAxisOfBest(best);
}
if(generation == 0){
// a)
(K == xAxis) ? K = 1 : K++;
// b)
//K = random(xAxis);
// c)
//if(K == 10) K = 25; else K = 10;
worldInit();
populationInit(3); // reinicialize 30% of population
fitness();
generation = m;
}else{
generation--;
}
selection();
crossover();
mutation();
fitness();
best = getBestIndividual();
}
printf("#generation: %4d, fitness: %4d\n", i, best.fitness);
if(!test)
printAxisOfBest(best);
}
void sortPopulation(struct POPULATION *population) {
struct INDIVIDUAL temp;
int i, j, k;
for (i = POPULATION_SIZE - 1; i >= 0; i--) {
for (j = 0; j < i; j++) {
k = getBestIndividual(population->population[j], population->population[j + 1]);
if(k == 2) {
temp = population->population[j];
population->population[j] = population->population[j + 1];
population->population[j + 1] = temp;
}
}
}
}
void CMA_ES_Population::generationUpdate()
{
std::vector<unsigned> indices = sortedIndices();
arx = sort(indices, arx, static_cast<unsigned>(mu));
arz = sort(indices, arz, static_cast<unsigned>(mu));
xmean = arx.transpose() * weights;
VectorXf zmean = arz.transpose() * weights;
ps = (1.f - cs) * ps + static_cast<float>(sqrt(cs * (2.f - cs) * mueff)) * (B* zmean);
pc = (1.f - cc) * pc + static_cast<float>(sqrt(cc * (2.f - cc) * mueff)) * (B* D* zmean);
float hsig = ps.norm() / sqrt(1 - pow(1 - cs, 2 * counteval / lambda_)) / chiN < 1.4 + 2.f / (n + 1);
C = (1 - c1 - cmu) * C
+ c1 * (pc * pc.transpose() + (1 - hsig) * cc * (2 - cc) * C)
+ cmu * (B* D* arz.transpose()) * weights.asDiagonal() * (B* D* arz.transpose()).transpose();
//OUTPUT_WARNING("" << C);
sigma *= exp((cs / damps) * (ps.norm() / chiN - 1));
ASSERT(!std::isnan(sigma));
if(counteval - eigenval > lambda_ / (c1 / cmu) / n / 10)
{
eigenval = counteval;
//C.triangularView<Eigen::Lower>() = C.triangularView<Eigen::Upper>().transpose();
Eigen::SelfAdjointEigenSolver<MatrixXf> es(C);
//OUTPUT_WARNING("Success" << es.info());
B = es.eigenvectors();
D = sqrt(es.eigenvalues().array()).matrix().asDiagonal();
}
curIndividualNr = 0;
configuration.initialization = getBestIndividual();
//save("nn_balancer_gen_" + std::to_string(generationCounter) + "_fit_" + std::to_string(static_cast<int>(getFitness(indices[0]))) + ".evo");
updateIndividuals();
somethingChanged = true;
}
void main()
{
int i;
int isUnique;
Individual best;
randomize();
worldInit();
// max 4 unique individuals, 5 and more last long to find
while(uniques < 4){
populationInit();
fitness();
for(i = 0; i < maxGenerations; i++){
selection();
crossover();
mutation();
fitness();
best = getBestIndividual();
if(best.fitness == maxFitness){
isUnique = unique(best);
if(isUnique){
theBestOf[uniques] = best;
uniques++;
}
break;
}
}
}
printAxisOfBest();
}
