LcVRPContest/Individual.cpp at master · plawinskig/LcVRPContest · GitHub

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#include "Individual.hpp"

#include "MyEvaluator.hpp"
#include <algorithm>

Individual::Individual(size_t genotype_size, int lower_bound, int upper_bound, std::mt19937 &engine)
	: genotype_(std::vector<int>(genotype_size)),
	fitness_(DEFAULT_FITNESS),
	cross_prob_(0.0),
	mut_prob_(0.0),
	has_fitness_(false)
{
	std::uniform_int_distribution<int> gene_dist(lower_bound, upper_bound);

	for (size_t i = 0; i < genotype_.size(); ++i)
	{
		genotype_[i] = gene_dist(engine);
	}

	std::uniform_real_distribution<float> mut_cross_dist(MIN_PROB, MAX_PROB);
	mut_prob_ = mut_cross_dist(engine);
	cross_prob_ = mut_cross_dist(engine);
}

Individual::Individual(const std::vector<int> &genotype, float cross_prob, float mut_prob)
	: genotype_(genotype),
	fitness_(DEFAULT_FITNESS),
	cross_prob_(cross_prob),
	mut_prob_(mut_prob),
	has_fitness_(false)
{
}

Individual::Individual(std::vector<int> &&genotype, float cross_prob, float mut_prob)
	: genotype_(std::move(genotype)),
	fitness_(DEFAULT_FITNESS),
	cross_prob_(cross_prob),
	mut_prob_(mut_prob),
	has_fitness_(false)
{
}

bool Individual::operator<(const Individual &other) const
{
	return fitness_ < other.fitness_;
}

double Individual::calcFitness(MyEvaluator &eval)
{
	if (!has_fitness_)
	{
		fitness_ = eval.evaluate(*this);
		has_fitness_ = true;
	}

	return fitness_;
}

void Individual::crossWith(const Individual &other, Individual &child1, Individual &child2, std::mt19937 &engine, float cross_prob) const
{
	std::bernoulli_distribution cross_trial(cross_prob > 0 ? cross_prob : cross_prob_);

	if (!cross_trial(engine))
	{
		child1 = *this;
		child2 = other;

		return;
	}

	std::uniform_int_distribution<int> cut_dist(1, static_cast<int>(genotype_.size()) - 1);
	int cut_idx = cut_dist(engine);

	std::copy(genotype_.begin(), genotype_.begin() + cut_idx, child1.genotype_.begin());
	std::copy(genotype_.begin() + cut_idx, genotype_.end(), child2.genotype_.begin() + cut_idx);

	std::copy(other.genotype_.begin() + cut_idx, other.genotype_.end(), child1.genotype_.begin() + cut_idx);
	std::copy(other.genotype_.begin(), other.genotype_.begin() + cut_idx, child2.genotype_.begin());

	child1.has_fitness_ = false;
	child2.has_fitness_ = false;

	std::bernoulli_distribution avg_strat_trial(0.3);

	if (avg_strat_trial(engine))
	{
		float avg = (mut_prob_ + other.mut_prob_) / 2;
		child1.mut_prob_ = avg;
		child2.mut_prob_ = avg;
	}
	else
	{
		child1.mut_prob_ = mut_prob_;
		child2.mut_prob_ = other.mut_prob_;
	}

	if (avg_strat_trial(engine))
	{
		float avg = (cross_prob_ + other.cross_prob_) / 2;
		child1.cross_prob_ = avg;
		child2.cross_prob_ = avg;
	}
	else
	{
		child1.cross_prob_ = cross_prob_;
		child2.cross_prob_ = other.cross_prob_;
	}
}

void Individual::mutateSimple(int lower_bound, int upper_bound, std::mt19937 &engine, float mut_prob)
{
	std::normal_distribution<float> param_delta_dist(0.0f, 0.01f);

	float cross_delta = param_delta_dist(engine);
	float mut_delta = param_delta_dist(engine);

	cross_prob_ += cross_delta;
	if (cross_prob_ < MIN_PROB) { cross_prob_ = MIN_PROB; }
	if (cross_prob_ > MAX_PROB) { cross_prob_ = MAX_PROB; }

	mut_prob_ += mut_delta;
	if (mut_prob_ < MIN_PROB) { mut_prob_ = MIN_PROB; }
	if (mut_prob_ > MAX_PROB) { mut_prob_ = MAX_PROB; }

	std::bernoulli_distribution mutation_trial(mut_prob > 0 ? mut_prob : mut_prob_);
	std::uniform_int_distribution<int> gene_dist(lower_bound, upper_bound);

	for (size_t i = 0; i < genotype_.size(); ++i)
	{
		if (mutation_trial(engine))
		{
			genotype_[i] = gene_dist(engine);
		}
	}

	has_fitness_ = false;
}

void Individual::mutateClose(MyEvaluator &eval, std::mt19937 &engine, float mut_prob)
{
	std::bernoulli_distribution mutation_trial(mut_prob > 0 ? mut_prob : mut_prob_);
	size_t size = genotype_.size();

	for (size_t i = 0; i < size; ++i)
	{
		if (mutation_trial(engine))
		{
			size_t take_from = i;
			double min_distance = eval.getMaxDistance();

			for (size_t j = 0; j < size; ++j)
			{
				if (i != j)
				{
					double distance = eval.getEdgeWeights().get(i, j);

					if (distance < min_distance)
					{
						min_distance = distance;
						take_from = j;
						has_fitness_ = false;
					}
				}
			}

			genotype_[i] = genotype_[take_from];
		}
	}
}