solidity/tools/yulPhaser/GeneticAlgorithms.cpp

177 lines
5.6 KiB
C++

/*
This file is part of solidity.
solidity 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.
solidity 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 solidity. If not, see <http://www.gnu.org/licenses/>.
*/
// SPDX-License-Identifier: GPL-3.0
#include <tools/yulPhaser/GeneticAlgorithms.h>
#include <tools/yulPhaser/Mutations.h>
#include <tools/yulPhaser/Selections.h>
#include <tools/yulPhaser/PairSelections.h>
using namespace std;
using namespace solidity;
using namespace solidity::phaser;
function<Crossover> phaser::buildCrossoverOperator(
CrossoverChoice _choice,
optional<double> _uniformCrossoverSwapChance
)
{
switch (_choice)
{
case CrossoverChoice::SinglePoint:
return randomPointCrossover();
case CrossoverChoice::TwoPoint:
return randomTwoPointCrossover();
case CrossoverChoice::Uniform:
assert(_uniformCrossoverSwapChance.has_value());
return uniformCrossover(_uniformCrossoverSwapChance.value());
default:
assertThrow(false, solidity::util::Exception, "Invalid CrossoverChoice value.");
};
}
function<SymmetricCrossover> phaser::buildSymmetricCrossoverOperator(
CrossoverChoice _choice,
optional<double> _uniformCrossoverSwapChance
)
{
switch (_choice)
{
case CrossoverChoice::SinglePoint:
return symmetricRandomPointCrossover();
case CrossoverChoice::TwoPoint:
return symmetricRandomTwoPointCrossover();
case CrossoverChoice::Uniform:
assert(_uniformCrossoverSwapChance.has_value());
return symmetricUniformCrossover(_uniformCrossoverSwapChance.value());
default:
assertThrow(false, solidity::util::Exception, "Invalid CrossoverChoice value.");
};
}
Population RandomAlgorithm::runNextRound(Population _population)
{
RangeSelection elite(0.0, m_options.elitePoolSize);
Population elitePopulation = _population.select(elite);
size_t replacementCount = _population.individuals().size() - elitePopulation.individuals().size();
return
move(elitePopulation) +
Population::makeRandom(
_population.fitnessMetric(),
replacementCount,
m_options.minChromosomeLength,
m_options.maxChromosomeLength
);
}
Population GenerationalElitistWithExclusivePools::runNextRound(Population _population)
{
double elitePoolSize = 1.0 - (m_options.mutationPoolSize + m_options.crossoverPoolSize);
RangeSelection elitePool(0.0, elitePoolSize);
RandomSelection mutationPoolFromElite(m_options.mutationPoolSize / elitePoolSize);
RandomPairSelection crossoverPoolFromElite(m_options.crossoverPoolSize / elitePoolSize);
std::function<Mutation> mutationOperator = alternativeMutations(
m_options.randomisationChance,
geneRandomisation(m_options.percentGenesToRandomise),
alternativeMutations(
m_options.deletionVsAdditionChance,
geneDeletion(m_options.percentGenesToAddOrDelete),
geneAddition(m_options.percentGenesToAddOrDelete)
)
);
std::function<Crossover> crossoverOperator = buildCrossoverOperator(
m_options.crossover,
m_options.uniformCrossoverSwapChance
);
return
_population.select(elitePool) +
_population.select(elitePool).mutate(mutationPoolFromElite, mutationOperator) +
_population.select(elitePool).crossover(crossoverPoolFromElite, crossoverOperator);
}
Population ClassicGeneticAlgorithm::runNextRound(Population _population)
{
Population elite = _population.select(RangeSelection(0.0, m_options.elitePoolSize));
Population rest = _population.select(RangeSelection(m_options.elitePoolSize, 1.0));
Population selectedPopulation = select(_population, rest.individuals().size());
std::function<SymmetricCrossover> crossoverOperator = buildSymmetricCrossoverOperator(
m_options.crossover,
m_options.uniformCrossoverSwapChance
);
Population crossedPopulation = Population::combine(
selectedPopulation.symmetricCrossoverWithRemainder(
PairsFromRandomSubset(m_options.crossoverChance),
crossoverOperator
)
);
std::function<Mutation> mutationOperator = mutationSequence({
geneRandomisation(m_options.mutationChance),
geneDeletion(m_options.deletionChance),
geneAddition(m_options.additionChance),
});
RangeSelection all(0.0, 1.0);
Population mutatedPopulation = crossedPopulation.mutate(all, mutationOperator);
return elite + mutatedPopulation;
}
Population ClassicGeneticAlgorithm::select(Population _population, size_t _selectionSize)
{
if (_population.individuals().size() == 0)
return _population;
size_t maxFitness = 0;
for (auto const& individual: _population.individuals())
maxFitness = max(maxFitness, individual.fitness);
size_t rouletteRange = 0;
for (auto const& individual: _population.individuals())
// Add 1 to make sure that every chromosome has non-zero probability of being chosen
rouletteRange += maxFitness + 1 - individual.fitness;
vector<Individual> selectedIndividuals;
for (size_t i = 0; i < _selectionSize; ++i)
{
size_t ball = SimulationRNG::uniformInt(0, rouletteRange - 1);
size_t cumulativeFitness = 0;
for (auto const& individual: _population.individuals())
{
size_t pocketSize = maxFitness + 1 - individual.fitness;
if (ball < cumulativeFitness + pocketSize)
{
selectedIndividuals.push_back(individual);
break;
}
cumulativeFitness += pocketSize;
}
}
assert(selectedIndividuals.size() == _selectionSize);
return Population(_population.fitnessMetric(), selectedIndividuals);
}