mirror of
https://github.com/ethereum/solidity
synced 2023-10-03 13:03:40 +00:00
408 lines
15 KiB
C++
408 lines
15 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/>.
|
|
*/
|
|
|
|
#include <test/yulPhaser/TestHelpers.h>
|
|
|
|
#include <tools/yulPhaser/FitnessMetrics.h>
|
|
#include <tools/yulPhaser/GeneticAlgorithms.h>
|
|
#include <tools/yulPhaser/Population.h>
|
|
|
|
#include <libsolutil/CommonIO.h>
|
|
|
|
#include <boost/test/unit_test.hpp>
|
|
|
|
#include <algorithm>
|
|
#include <vector>
|
|
|
|
using namespace std;
|
|
using namespace boost::unit_test::framework;
|
|
using namespace boost::test_tools;
|
|
using namespace solidity::util;
|
|
|
|
namespace solidity::phaser::test
|
|
{
|
|
|
|
class GeneticAlgorithmFixture
|
|
{
|
|
protected:
|
|
shared_ptr<FitnessMetric> m_fitnessMetric = make_shared<ChromosomeLengthMetric>();
|
|
};
|
|
|
|
class ClassicGeneticAlgorithmFixture: public GeneticAlgorithmFixture
|
|
{
|
|
protected:
|
|
ClassicGeneticAlgorithm::Options m_options = {
|
|
/* elitePoolSize = */ 0.0,
|
|
/* crossoverChance = */ 0.0,
|
|
/* mutationChance = */ 0.0,
|
|
/* deletionChance = */ 0.0,
|
|
/* additionChance = */ 0.0,
|
|
/* CrossoverChoice = */ CrossoverChoice::SinglePoint,
|
|
/* uniformCrossoverSwapChance= */ 0.5,
|
|
};
|
|
};
|
|
|
|
BOOST_AUTO_TEST_SUITE(Phaser)
|
|
BOOST_AUTO_TEST_SUITE(GeneticAlgorithmsTest)
|
|
BOOST_AUTO_TEST_SUITE(RandomAlgorithmTest)
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_preserve_elite_and_randomise_rest_of_population, GeneticAlgorithmFixture)
|
|
{
|
|
auto population = Population::makeRandom(m_fitnessMetric, 4, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
|
|
assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
|
|
RandomAlgorithm algorithm({0.5, 1, 1});
|
|
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{1, 1, 1, 1, 3, 3, 3, 3}));
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_not_replace_elite_with_worse_individuals, GeneticAlgorithmFixture)
|
|
{
|
|
auto population = Population::makeRandom(m_fitnessMetric, 4, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
|
|
assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
|
|
RandomAlgorithm algorithm({0.5, 7, 7});
|
|
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{3, 3, 3, 3, 7, 7, 7, 7}));
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_replace_all_chromosomes_if_zero_size_elite, GeneticAlgorithmFixture)
|
|
{
|
|
auto population = Population::makeRandom(m_fitnessMetric, 4, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
|
|
assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
|
|
RandomAlgorithm algorithm({0.0, 1, 1});
|
|
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{1, 1, 1, 1, 1, 1, 1, 1}));
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_not_replace_any_chromosomes_if_whole_population_is_the_elite, GeneticAlgorithmFixture)
|
|
{
|
|
auto population = Population::makeRandom(m_fitnessMetric, 4, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
|
|
assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
|
|
RandomAlgorithm algorithm({1.0, 1, 1});
|
|
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
|
|
}
|
|
|
|
BOOST_AUTO_TEST_SUITE_END()
|
|
BOOST_AUTO_TEST_SUITE(GenerationalElitistWithExclusivePoolsTest)
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_preserve_elite_and_regenerate_rest_of_population, GeneticAlgorithmFixture)
|
|
{
|
|
auto population = Population::makeRandom(m_fitnessMetric, 6, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
|
|
assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 3, 3, 5, 5, 5, 5}));
|
|
|
|
GenerationalElitistWithExclusivePools::Options options = {
|
|
/* mutationPoolSize = */ 0.2,
|
|
/* crossoverPoolSize = */ 0.2,
|
|
/* randomisationChance = */ 0.0,
|
|
/* deletionVsAdditionChance = */ 1.0,
|
|
/* percentGenesToRandomise = */ 0.0,
|
|
/* percentGenesToAddOrDelete = */ 1.0,
|
|
/* CrossoverChoice = */ CrossoverChoice::SinglePoint,
|
|
/* uniformCrossoverSwapChance= */ 0.5,
|
|
};
|
|
GenerationalElitistWithExclusivePools algorithm(options);
|
|
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{0, 0, 3, 3, 3, 3, 3, 3, 3, 3}));
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_not_replace_elite_with_worse_individuals, GeneticAlgorithmFixture)
|
|
{
|
|
auto population = Population::makeRandom(m_fitnessMetric, 6, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
|
|
assert(chromosomeLengths(population) == (vector<size_t>{3, 3, 3, 3, 3, 3, 5, 5, 5, 5}));
|
|
|
|
GenerationalElitistWithExclusivePools::Options options = {
|
|
/* mutationPoolSize = */ 0.2,
|
|
/* crossoverPoolSize = */ 0.2,
|
|
/* randomisationChance = */ 0.0,
|
|
/* deletionVsAdditionChance = */ 0.0,
|
|
/* percentGenesToRandomise = */ 0.0,
|
|
/* percentGenesToAddOrDelete = */ 1.0,
|
|
/* CrossoverChoice = */ CrossoverChoice::SinglePoint,
|
|
/* uniformCrossoverSwapChance= */ 0.5,
|
|
};
|
|
GenerationalElitistWithExclusivePools algorithm(options);
|
|
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{3, 3, 3, 3, 3, 3, 3, 3, 7, 7}));
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_generate_individuals_in_the_crossover_pool_by_mutating_the_elite, GeneticAlgorithmFixture)
|
|
{
|
|
auto population = Population::makeRandom(m_fitnessMetric, 20, 5, 5);
|
|
|
|
GenerationalElitistWithExclusivePools::Options options = {
|
|
/* mutationPoolSize = */ 0.8,
|
|
/* crossoverPoolSize = */ 0.0,
|
|
/* randomisationChance = */ 0.5,
|
|
/* deletionVsAdditionChance = */ 0.5,
|
|
/* percentGenesToRandomise = */ 1.0,
|
|
/* percentGenesToAddOrDelete = */ 1.0,
|
|
/* CrossoverChoice = */ CrossoverChoice::SinglePoint,
|
|
/* uniformCrossoverSwapChance= */ 0.5,
|
|
};
|
|
GenerationalElitistWithExclusivePools algorithm(options);
|
|
|
|
SimulationRNG::reset(1);
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
BOOST_TEST((
|
|
chromosomeLengths(newPopulation) ==
|
|
vector<size_t>{0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 11, 11, 11}
|
|
));
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_generate_individuals_in_the_crossover_pool_by_crossing_over_the_elite, GeneticAlgorithmFixture)
|
|
{
|
|
auto population = (
|
|
Population(m_fitnessMetric, {Chromosome("aa"), Chromosome("ff")}) +
|
|
Population::makeRandom(m_fitnessMetric, 8, 6, 6)
|
|
);
|
|
assert((chromosomeLengths(population) == vector<size_t>{2, 2, 6, 6, 6, 6, 6, 6, 6, 6}));
|
|
|
|
GenerationalElitistWithExclusivePools::Options options = {
|
|
/* mutationPoolSize = */ 0.0,
|
|
/* crossoverPoolSize = */ 0.8,
|
|
/* randomisationChance = */ 0.0,
|
|
/* deletionVsAdditionChance = */ 0.0,
|
|
/* percentGenesToRandomise = */ 0.0,
|
|
/* percentGenesToAddOrDelete = */ 0.0,
|
|
/* CrossoverChoice = */ CrossoverChoice::SinglePoint,
|
|
/* uniformCrossoverSwapChance= */ 0.5,
|
|
};
|
|
GenerationalElitistWithExclusivePools algorithm(options);
|
|
|
|
SimulationRNG::reset(1);
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
vector<Individual> const& newIndividuals = newPopulation.individuals();
|
|
BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{2, 2, 2, 2, 2, 2, 2, 2, 2, 2}));
|
|
for (auto& individual: newIndividuals)
|
|
BOOST_TEST((
|
|
individual.chromosome == Chromosome("aa") ||
|
|
individual.chromosome == Chromosome("af") ||
|
|
individual.chromosome == Chromosome("fa") ||
|
|
individual.chromosome == Chromosome("ff")
|
|
));
|
|
BOOST_TEST(any_of(newIndividuals.begin() + 2, newIndividuals.end(), [](auto& individual){
|
|
return individual.chromosome != Chromosome("aa") && individual.chromosome != Chromosome("ff");
|
|
}));
|
|
}
|
|
|
|
BOOST_AUTO_TEST_SUITE_END()
|
|
BOOST_AUTO_TEST_SUITE(ClassicGeneticAlgorithmTest)
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_select_individuals_with_probability_proportional_to_fitness, ClassicGeneticAlgorithmFixture)
|
|
{
|
|
constexpr double relativeTolerance = 0.1;
|
|
constexpr size_t populationSize = 1000;
|
|
assert(populationSize % 4 == 0 && "Choose a number divisible by 4 for this test");
|
|
|
|
auto population =
|
|
Population::makeRandom(m_fitnessMetric, populationSize / 4, 0, 0) +
|
|
Population::makeRandom(m_fitnessMetric, populationSize / 4, 1, 1) +
|
|
Population::makeRandom(m_fitnessMetric, populationSize / 4, 2, 2) +
|
|
Population::makeRandom(m_fitnessMetric, populationSize / 4, 3, 3);
|
|
|
|
map<size_t, double> expectedProbabilities = {
|
|
{0, 4.0 / (4 + 3 + 2 + 1)},
|
|
{1, 3.0 / (4 + 3 + 2 + 1)},
|
|
{2, 2.0 / (4 + 3 + 2 + 1)},
|
|
{3, 1.0 / (4 + 3 + 2 + 1)},
|
|
};
|
|
double const expectedValue = (
|
|
0.0 * expectedProbabilities[0] +
|
|
1.0 * expectedProbabilities[1] +
|
|
2.0 * expectedProbabilities[2] +
|
|
3.0 * expectedProbabilities[3]
|
|
);
|
|
double const variance = (
|
|
(0.0 - expectedValue) * (0.0 - expectedValue) * expectedProbabilities[0] +
|
|
(1.0 - expectedValue) * (1.0 - expectedValue) * expectedProbabilities[1] +
|
|
(2.0 - expectedValue) * (2.0 - expectedValue) * expectedProbabilities[2] +
|
|
(3.0 - expectedValue) * (3.0 - expectedValue) * expectedProbabilities[3]
|
|
);
|
|
|
|
ClassicGeneticAlgorithm algorithm(m_options);
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
BOOST_TEST(newPopulation.individuals().size() == population.individuals().size());
|
|
|
|
vector<size_t> newFitness = chromosomeLengths(newPopulation);
|
|
BOOST_TEST(abs(mean(newFitness) - expectedValue) < expectedValue * relativeTolerance);
|
|
BOOST_TEST(abs(meanSquaredError(newFitness, expectedValue) - variance) < variance * relativeTolerance);
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_select_only_individuals_existing_in_the_original_population, ClassicGeneticAlgorithmFixture)
|
|
{
|
|
constexpr size_t populationSize = 1000;
|
|
auto population = Population::makeRandom(m_fitnessMetric, populationSize, 1, 10);
|
|
|
|
set<string> originalSteps;
|
|
for (auto const& individual: population.individuals())
|
|
originalSteps.insert(toString(individual.chromosome));
|
|
|
|
ClassicGeneticAlgorithm algorithm(m_options);
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
for (auto const& individual: newPopulation.individuals())
|
|
BOOST_TEST(originalSteps.count(toString(individual.chromosome)) == 1);
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_do_crossover, ClassicGeneticAlgorithmFixture)
|
|
{
|
|
auto population = Population(m_fitnessMetric, {
|
|
Chromosome("aa"), Chromosome("aa"), Chromosome("aa"),
|
|
Chromosome("ff"), Chromosome("ff"), Chromosome("ff"),
|
|
Chromosome("gg"), Chromosome("gg"), Chromosome("gg"),
|
|
});
|
|
|
|
set<string> originalSteps{"aa", "ff", "gg"};
|
|
set<string> crossedSteps{"af", "fa", "fg", "gf", "ga", "ag"};
|
|
|
|
m_options.crossoverChance = 0.8;
|
|
ClassicGeneticAlgorithm algorithm(m_options);
|
|
|
|
SimulationRNG::reset(1);
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
size_t totalCrossed = 0;
|
|
size_t totalUnchanged = 0;
|
|
for (auto const& individual: newPopulation.individuals())
|
|
{
|
|
totalCrossed += crossedSteps.count(toString(individual.chromosome));
|
|
totalUnchanged += originalSteps.count(toString(individual.chromosome));
|
|
}
|
|
BOOST_TEST(totalCrossed + totalUnchanged == newPopulation.individuals().size());
|
|
BOOST_TEST(totalCrossed >= 2);
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_do_mutation, ClassicGeneticAlgorithmFixture)
|
|
{
|
|
m_options.mutationChance = 0.6;
|
|
ClassicGeneticAlgorithm algorithm(m_options);
|
|
|
|
constexpr size_t populationSize = 1000;
|
|
constexpr double relativeTolerance = 0.05;
|
|
double const expectedValue = m_options.mutationChance;
|
|
double const variance = m_options.mutationChance * (1 - m_options.mutationChance);
|
|
|
|
Chromosome chromosome("aaaaaaaaaa");
|
|
vector<Chromosome> chromosomes(populationSize, chromosome);
|
|
Population population(m_fitnessMetric, chromosomes);
|
|
|
|
SimulationRNG::reset(1);
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
vector<size_t> bernoulliTrials;
|
|
for (auto const& individual: newPopulation.individuals())
|
|
{
|
|
string steps = toString(individual.chromosome);
|
|
for (char step: steps)
|
|
bernoulliTrials.push_back(static_cast<size_t>(step != 'a'));
|
|
}
|
|
|
|
BOOST_TEST(abs(mean(bernoulliTrials) - expectedValue) < expectedValue * relativeTolerance);
|
|
BOOST_TEST(abs(meanSquaredError(bernoulliTrials, expectedValue) - variance) < variance * relativeTolerance);
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_do_deletion, ClassicGeneticAlgorithmFixture)
|
|
{
|
|
m_options.deletionChance = 0.6;
|
|
ClassicGeneticAlgorithm algorithm(m_options);
|
|
|
|
constexpr size_t populationSize = 1000;
|
|
constexpr double relativeTolerance = 0.05;
|
|
double const expectedValue = m_options.deletionChance;
|
|
double const variance = m_options.deletionChance * (1 - m_options.deletionChance);
|
|
|
|
Chromosome chromosome("aaaaaaaaaa");
|
|
vector<Chromosome> chromosomes(populationSize, chromosome);
|
|
Population population(m_fitnessMetric, chromosomes);
|
|
|
|
SimulationRNG::reset(1);
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
vector<size_t> bernoulliTrials;
|
|
for (auto const& individual: newPopulation.individuals())
|
|
{
|
|
string steps = toString(individual.chromosome);
|
|
for (size_t i = 0; i < chromosome.length(); ++i)
|
|
bernoulliTrials.push_back(static_cast<size_t>(i >= steps.size()));
|
|
}
|
|
|
|
BOOST_TEST(abs(mean(bernoulliTrials) - expectedValue) < expectedValue * relativeTolerance);
|
|
BOOST_TEST(abs(meanSquaredError(bernoulliTrials, expectedValue) - variance) < variance * relativeTolerance);
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_do_addition, ClassicGeneticAlgorithmFixture)
|
|
{
|
|
m_options.additionChance = 0.6;
|
|
ClassicGeneticAlgorithm algorithm(m_options);
|
|
|
|
constexpr size_t populationSize = 1000;
|
|
constexpr double relativeTolerance = 0.05;
|
|
double const expectedValue = m_options.additionChance;
|
|
double const variance = m_options.additionChance * (1 - m_options.additionChance);
|
|
|
|
Chromosome chromosome("aaaaaaaaaa");
|
|
vector<Chromosome> chromosomes(populationSize, chromosome);
|
|
Population population(m_fitnessMetric, chromosomes);
|
|
|
|
SimulationRNG::reset(1);
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
vector<size_t> bernoulliTrials;
|
|
for (auto const& individual: newPopulation.individuals())
|
|
{
|
|
string steps = toString(individual.chromosome);
|
|
for (size_t i = 0; i < chromosome.length() + 1; ++i)
|
|
{
|
|
BOOST_REQUIRE(chromosome.length() <= steps.size() && steps.size() <= 2 * chromosome.length() + 1);
|
|
bernoulliTrials.push_back(static_cast<size_t>(i < steps.size() - chromosome.length()));
|
|
}
|
|
}
|
|
|
|
BOOST_TEST(abs(mean(bernoulliTrials) - expectedValue) < expectedValue * relativeTolerance);
|
|
BOOST_TEST(abs(meanSquaredError(bernoulliTrials, expectedValue) - variance) < variance * relativeTolerance);
|
|
}
|
|
|
|
BOOST_FIXTURE_TEST_CASE(runNextRound_should_preserve_elite, ClassicGeneticAlgorithmFixture)
|
|
{
|
|
auto population = Population::makeRandom(m_fitnessMetric, 4, 3, 3) + Population::makeRandom(m_fitnessMetric, 6, 5, 5);
|
|
assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5, 5, 5}));
|
|
|
|
m_options.elitePoolSize = 0.5;
|
|
m_options.deletionChance = 1.0;
|
|
ClassicGeneticAlgorithm algorithm(m_options);
|
|
Population newPopulation = algorithm.runNextRound(population);
|
|
|
|
BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{0, 0, 0, 0, 0, 3, 3, 3, 3, 5}));
|
|
}
|
|
|
|
BOOST_AUTO_TEST_SUITE_END()
|
|
BOOST_AUTO_TEST_SUITE_END()
|
|
BOOST_AUTO_TEST_SUITE_END()
|
|
|
|
}
|