// Epoch Method
void GeneticAlgorithm::Epoch() {
// Update the fitness scores
UpdateFitnessScores();
// Create a new population
int iNewOffspring = 0;
// Create a placeholder for the offspring
vector<Genome> vOffspringGenomes;
// Loop through the babies
while (iNewOffspring < mPopulationSize) {
// Select 2 parents
Genome vMother = Selection();
Genome vFather = Selection();
// Offspring placeholders
Genome vOffspringAlpha, vOffspringBeta;
// Crossover
Crossover(vMother.vBits, vFather.vBits, vOffspringAlpha.vBits, vOffspringBeta.vBits);
// Mutate
Mutate(vOffspringAlpha.vBits);
Mutate(vOffspringBeta.vBits);
// Add the offspring to the new population
vOffspringGenomes.push_back(vOffspringAlpha);
vOffspringGenomes.push_back(vOffspringBeta);
// Increment the offspring
iNewOffspring += 2;
}
// Copy the offspring back into the initial population
mGenomes = vOffspringGenomes;
// Increment the generations
++mGeneration;
}
//--------------------------------Epoch---------------------------------
//
// This is the workhorse of the GA. It first updates the fitness
// scores of the population then creates a new population of
// genomes using the Selection, Croosover and Mutation operators
// we have discussed
//----------------------------------------------------------------------
void CgaBob::Epoch()
{
UpdateFitnessScores();
//Now to create a new population
int NewBabies = 0;
//create some storage for the baby genomes
vector<SGenome> vecBabyGenomes;
while (NewBabies < m_iPopSize)
{
//select 2 parents
SGenome mum = RouletteWheelSelection();
SGenome dad = RouletteWheelSelection();
//operator - crossover
SGenome baby1, baby2;
Crossover(mum.vecBits, dad.vecBits, baby1.vecBits, baby2.vecBits);
//operator - mutate
Mutate(baby1.vecBits);
Mutate(baby2.vecBits);
//add to new population
vecBabyGenomes.push_back(baby1);
vecBabyGenomes.push_back(baby2);
NewBabies += 2;
}
//copy babies back into starter population
m_vecGenomes = vecBabyGenomes;
//increment the generation counter
++m_iGeneration;
}
