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Created by einar on 2/27/17.
Copyright (C) 2017 Einar J.M. Baumann <einar.baumann@gmail.com>
This file is part of the FieldOpt project.
FieldOpt is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
FieldOpt is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with FieldOpt. If not, see <http://www.gnu.org/licenses/>.
******************************************************************************/
#include "GeneticAlgorithm.h"
#include "Utilities/math.hpp"
#include "Utilities/stringhelpers.hpp"
#include <math.h>
namespace Optimization {
namespace Optimizers {
GeneticAlgorithm::GeneticAlgorithm(Settings::Optimizer *settings,
Case *base_case,
Model::Properties::VariablePropertyContainer *variables,
Reservoir::Grid::Grid *grid,
Logger *logger
)
: Optimizer(settings, base_case, variables, grid, logger) {
n_vars_ = variables->ContinousVariableSize();
gen_ = get_random_generator();
max_generations_ = settings->parameters().max_generations;
if (settings->parameters().population_size < 0)
population_size_ = std::min(10*n_vars_, 100);
else population_size_ = settings->parameters().population_size;
if (population_size_ % 2 != 0) population_size_--; // Make sure its an even number
p_crossover_ = settings->parameters().p_crossover;
decay_rate_ = settings->parameters().decay_rate;
mutation_strength_ = settings->parameters().mutation_strength;
if (constraint_handler_->HasBoundaryConstraints()) {
lower_bound_ = constraint_handler_->GetLowerBounds(base_case->GetRealVarIdVector());
upper_bound_ = constraint_handler_->GetUpperBounds(base_case->GetRealVarIdVector());
if (verbosity_level_ > 1) {
cout << "Using bounds from constraints: " << endl;
cout << vec_to_str(vector<double>(lower_bound_.data(), lower_bound_.data() + lower_bound_.size()));
cout << endl;
cout << vec_to_str(vector<double>(upper_bound_.data(), upper_bound_.data() + upper_bound_.size()));
cout << endl;
}
}
else {
lower_bound_.resize(n_vars_);
upper_bound_.resize(n_vars_);
lower_bound_.fill(settings->parameters().lower_bound);
upper_bound_.fill(settings->parameters().upper_bound);
}
for (int i = 0; i < population_size_; ++i) {
auto new_case = generateRandomCase();
population_.push_back(Chromosome(new_case));
case_handler_->AddNewCase(new_case);
}
if (verbosity_level_ > 1) {
cout << "Initial ";
printPopulation();
}
}
Optimizer::TerminationCondition GeneticAlgorithm::IsFinished() {
TerminationCondition tc = NOT_FINISHED;
if (case_handler_->CasesBeingEvaluated().size() > 0)
return tc;
if (iteration_ >= max_generations_)
tc = MAX_ITERATIONS_REACHED;
else if (case_handler_->NumberSimulated() > max_evaluations_)
tc = MAX_EVALS_REACHED;
if (tc != NOT_FINISHED) {
cout << "Generations at termination: " << iteration_ << endl;
population_ = sortPopulation(population_);
logger_->AddEntry(this);
logger_->AddEntry(new Summary(this, tc));
}
return tc;
}
GeneticAlgorithm::Chromosome::Chromosome(Case *c) {
case_pointer = c;
rea_vars = c->GetRealVarVector();
}
void GeneticAlgorithm::Chromosome::createNewCase() {
Case *new_case = new Case(case_pointer);
new_case->SetRealVarValues(rea_vars);
case_pointer = new_case;
}
void GeneticAlgorithm::printPopulation(vector<Chromosome> population) const {
if (population.size() == 0)
population = population_;
cout << "Population:" << endl;
for (int i = 0; i < population.size(); ++i) {
cout << "\t" << i << "\t";
printChromosome(population[i]);
}
}
void GeneticAlgorithm::printChromosome(Chromosome &chrom) const {
printf("%4.2f\t\t", chrom.ofv());
for (int i = 0; i < n_vars_; ++i) {
printf("%2.4f\t", chrom.rea_vars(i));
}
cout << endl;
}
vector<GeneticAlgorithm::Chromosome> GeneticAlgorithm::sortPopulation(vector<Chromosome> population) {
std::sort(population.begin(), population.end(), [&](Chromosome c1, Chromosome c2) {
return isBetter(c1.case_pointer, c2.case_pointer);
});
return population;
}
Case *GeneticAlgorithm::generateRandomCase() {
auto new_case = new Case(GetTentativeBestCase());
Eigen::VectorXd erands(n_vars_);
for (int i = 0; i < n_vars_; ++i) {
erands(i) = random_doubles(gen_, lower_bound_(i), upper_bound_(i), 1)[0];
}
new_case->SetRealVarValues(erands);
return new_case;
}
void GeneticAlgorithm::penalizeInitialGeneration() {
initializeNormalizers();
for (auto chrom : population_) {
double pen_ofv = PenalizedOFV(chrom.case_pointer);
chrom.case_pointer->set_objective_function_value(pen_ofv);
}
}
}
}
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