void GeneticAlgorithm::run() {
Population p = createBasePopulation();
p.process();
generations.push_back(p);
double fitness = 0.001;
int generation = 0;
while(generation < params.generations){
//p.printChromos();
generation++;
p = p.tournament_selection(generation);
p.crossover_selection(params.crossoverPercent);
p.mutation(params.mutationPercent);
p.process();
p.calcDiversity();
//p.printBestCandidate();
// std::cout << "Min Fitness: " << p.minFitness << std::endl;
// std::cout << "Max Fitness: " << p.maxFitness << std::endl;
generations.push_back(p);
}
generations.back().printBestCandidate();
//resultToFile();
resultAsJson();
}
Environment::Environment(Point start, Point target, int type, int populationSize, float f, float cr, int movementSteps) {
this->start.x = start.x;
this->start.y = start.y;
this->target.x = target.x;
this->target.y = target.y;
this->type = type;
this->movementSteps = movementSteps;
this->f = f;
this->cr = cr;
startPopulationSize = populationSize;
createBasePopulation();
}
