virtual void run(unsigned int generations,
unsigned int logFrequency = 100) {
// Generation: create the new members
for (auto i = 0U; i < PopSize; ++i) {
population.push_back(generator());
}
for (auto generation = 0U; generation < generations; ++generation) {
// Crossover: Add missing members
auto popSizePostSelection = population.size();
while (population.size() < PopSize) {
population.push_back(
crossover(population[random_uint(popSizePostSelection)],
population[random_uint(popSizePostSelection)]));
}
// Mutation: Mutate at least rate*popsize members
for (size_t i = 0; i < PopSize * mutationRate; ++i) {
auto index = random_uint(PopSize);
mutator(population[index]);
}
// Selection: Destroy the least fit members
selector(population, evaluator);
if (evaluator(population[0]) > bestScore) {
bestMember = population[0];
bestScore = evaluator(population[0]);
}
if (generation % logFrequency == 0) {
std::cout << "Generation(" << generation
<< ") - Fitness:" << bestScore << std::endl;
}
}
std::cout << "Best: ";
for (const auto& allele : bestMember) {
std::cout << allele << " ";
}
std::cout << std::endl << "Fitness: " << evaluator(bestMember)
<< std::endl;
}
