void Queen::defaultAction(float frameTime){
if(cooldown == 0){
Reproduce(*getCenter(),species);
cooldown += queenNS::BIRTH_RATE;
}
if(pher == nullptr)
{
pher = world->spawnPher(*getCenter(),Signal(SignalType::queen,*getCenter(),species));
}
else
{
pher->refresh();
pher->setSignal(Signal(SignalType::queen,*getCenter(),species));
}
}
double CNE::Optimize(DecomposableFunctionType& function, arma::mat& iterate)
{
// Make sure for evolution to work at least four candidates are present.
if (populationSize < 4)
{
throw std::logic_error("CNE::Optimize(): population size should be at least"
" 4!");
}
// Find the number of elite canditates from population.
numElite = floor(selectPercent * populationSize);
// Making sure we have even number of candidates to remove and create.
if ((populationSize - numElite) % 2 != 0)
numElite--;
// Terminate if two parents can not be created.
if (numElite < 2)
{
throw std::logic_error("CNE::Optimize(): unable to select two parents. "
"Increase selection percentage.");
}
// Terminate if at least two childs are not possible.
if ((populationSize - numElite) < 2)
{
throw std::logic_error("CNE::Optimize(): no space to accomodate even 2 "
"children. Increase population size.");
}
// Set the population size and fill random values [0,1].
population = arma::randu(iterate.n_rows, iterate.n_cols, populationSize);
// Store the number of elements in a cube slice or a matrix column.
elements = population.n_rows * population.n_cols;
// initializing helper variables.
fitnessValues.set_size(populationSize);
Log::Info << "CNE initialized successfully. Optimization started."
<< std::endl;
// Find the fitness before optimization using given iterate parameters.
size_t lastBestFitness = function.Evaluate(iterate);
// Iterate until maximum number of generations is obtained.
for (size_t gen = 1; gen <= maxGenerations; gen++)
{
// Calculating fitness values of all candidates.
for (size_t i = 0; i < populationSize; i++)
{
// Select a candidate and insert the parameters in the function.
iterate = population.slice(i);
// Find fitness of candidate.
fitnessValues[i] = function.Evaluate(iterate);
}
Log::Info << "Generation number: " << gen << " best fitness = "
<< fitnessValues.min() << std::endl;
// Create next generation of species.
Reproduce();
// Check for termination criteria.
if (tolerance >= fitnessValues.min())
{
Log::Info << "CNE::Optimize(): terminating. Given fitness criteria "
<< tolerance << " > " << fitnessValues.min() << "." << std::endl;
break;
}
// Check for termination criteria.
if (lastBestFitness - fitnessValues.min() < objectiveChange)
{
Log::Info << "CNE::Optimize(): terminating. Fitness history change "
<< (lastBestFitness - fitnessValues.min())
<< " < " << objectiveChange << "." << std::endl;
break;
}
// Store the best fitness of present generation.
lastBestFitness = fitnessValues.min();
}
// Set the best candidate into the network parameters.
iterate = population.slice(index(0));
return function.Evaluate(iterate);
}
