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https://github.com/ethereum/solidity
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[yul-phaser] Population: Store fitness metric rather than program directly
- In the console app use ProgramSize metric when creating the population.
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@ -58,35 +58,7 @@ namespace
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class PopulationFixture
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{
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protected:
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PopulationFixture():
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m_sourceStream(SampleSourceCode, ""),
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m_program(Program::load(m_sourceStream)) {}
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static constexpr char SampleSourceCode[] =
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"{\n"
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" let factor := 13\n"
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" {\n"
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" if factor\n"
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" {\n"
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" let variable := add(1, 2)\n"
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" }\n"
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" let result := factor\n"
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" }\n"
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" let something := 6\n"
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" {\n"
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" {\n"
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" {\n"
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" let value := 15\n"
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" }\n"
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" }\n"
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" }\n"
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" let something_else := mul(mul(something, 1), add(factor, 0))\n"
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" if 1 { let x := 1 }\n"
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" if 0 { let y := 2 }\n"
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"}\n";
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CharStream m_sourceStream;
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Program m_program;
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shared_ptr<FitnessMetric> m_fitnessMetric = make_shared<ChromosomeLengthMetric>();
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};
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BOOST_AUTO_TEST_SUITE(Phaser)
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@ -128,7 +100,7 @@ BOOST_FIXTURE_TEST_CASE(constructor_should_copy_chromosomes_and_not_compute_fitn
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Chromosome::makeRandom(5),
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Chromosome::makeRandom(10),
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};
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Population population(m_program, chromosomes);
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Population population(m_fitnessMetric, chromosomes);
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BOOST_TEST(population.individuals().size() == 2);
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BOOST_TEST(population.individuals()[0].chromosome == chromosomes[0]);
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@ -145,7 +117,7 @@ BOOST_FIXTURE_TEST_CASE(makeRandom_should_get_chromosome_lengths_from_specified_
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assert(chromosomeCount % maxLength == 0);
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auto nextLength = [counter = 0, maxLength]() mutable { return counter++ % maxLength; };
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auto population = Population::makeRandom(m_program, chromosomeCount, nextLength);
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auto population = Population::makeRandom(m_fitnessMetric, chromosomeCount, nextLength);
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// We can't rely on the order since the population sorts its chromosomes immediately but
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// we can check the number of occurrences of each length.
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@ -161,7 +133,7 @@ BOOST_FIXTURE_TEST_CASE(makeRandom_should_get_chromosome_lengths_from_specified_
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BOOST_FIXTURE_TEST_CASE(makeRandom_should_get_chromosome_lengths_from_specified_range, PopulationFixture)
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{
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auto population = Population::makeRandom(m_program, 100, 5, 10);
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auto population = Population::makeRandom(m_fitnessMetric, 100, 5, 10);
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BOOST_TEST(all_of(
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population.individuals().begin(),
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population.individuals().end(),
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@ -177,7 +149,7 @@ BOOST_FIXTURE_TEST_CASE(makeRandom_should_use_random_chromosome_length, Populati
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constexpr int maxLength = 10;
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constexpr double relativeTolerance = 0.05;
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auto population = Population::makeRandom(m_program, populationSize, minLength, maxLength);
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auto population = Population::makeRandom(m_fitnessMetric, populationSize, minLength, maxLength);
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vector<size_t> samples = chromosomeLengths(population);
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const double expectedValue = (maxLength + minLength) / 2.0;
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@ -195,7 +167,7 @@ BOOST_FIXTURE_TEST_CASE(makeRandom_should_return_population_with_random_chromoso
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constexpr double relativeTolerance = 0.01;
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map<string, size_t> stepIndices = enumerateOptmisationSteps();
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auto population = Population::makeRandom(m_program, populationSize, chromosomeLength, chromosomeLength);
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auto population = Population::makeRandom(m_fitnessMetric, populationSize, chromosomeLength, chromosomeLength);
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vector<size_t> samples;
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for (auto& individual: population.individuals())
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@ -211,7 +183,7 @@ BOOST_FIXTURE_TEST_CASE(makeRandom_should_return_population_with_random_chromoso
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BOOST_FIXTURE_TEST_CASE(makeRandom_should_not_compute_fitness, PopulationFixture)
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{
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auto population = Population::makeRandom(m_program, 3, 5, 10);
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auto population = Population::makeRandom(m_fitnessMetric, 3, 5, 10);
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BOOST_TEST(all_of(population.individuals().begin(), population.individuals().end(), fitnessNotSet));
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}
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@ -219,7 +191,7 @@ BOOST_FIXTURE_TEST_CASE(makeRandom_should_not_compute_fitness, PopulationFixture
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BOOST_FIXTURE_TEST_CASE(run_should_evaluate_fitness, PopulationFixture)
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{
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stringstream output;
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auto population = Population::makeRandom(m_program, 5, 5, 10);
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auto population = Population::makeRandom(m_fitnessMetric, 5, 5, 10);
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assert(all_of(population.individuals().begin(), population.individuals().end(), fitnessNotSet));
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population.run(1, output);
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@ -237,11 +209,11 @@ BOOST_FIXTURE_TEST_CASE(run_should_not_make_fitness_of_top_chromosomes_worse, Po
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Chromosome(vector<string>{UnusedPruner::name}),
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Chromosome(vector<string>{StructuralSimplifier::name, BlockFlattener::name}),
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};
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Population population(m_program, chromosomes);
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Population population(m_fitnessMetric, chromosomes);
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size_t initialTopFitness[2] = {
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Population::measureFitness(chromosomes[0], m_program),
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Population::measureFitness(chromosomes[1], m_program),
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m_fitnessMetric->evaluate(chromosomes[0]),
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m_fitnessMetric->evaluate(chromosomes[1]),
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};
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for (int i = 0; i < 6; ++i)
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@ -264,9 +236,9 @@ BOOST_FIXTURE_TEST_CASE(run_should_not_make_fitness_of_top_chromosomes_worse, Po
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BOOST_FIXTURE_TEST_CASE(plus_operator_should_add_two_populations, PopulationFixture)
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{
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BOOST_CHECK_EQUAL(
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Population(m_program, {Chromosome("ac"), Chromosome("cx")}) +
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Population(m_program, {Chromosome("g"), Chromosome("h"), Chromosome("iI")}),
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Population(m_program, {Chromosome("ac"), Chromosome("cx"), Chromosome("g"), Chromosome("h"), Chromosome("iI")})
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Population(m_fitnessMetric, {Chromosome("ac"), Chromosome("cx")}) +
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Population(m_fitnessMetric, {Chromosome("g"), Chromosome("h"), Chromosome("iI")}),
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Population(m_fitnessMetric, {Chromosome("ac"), Chromosome("cx"), Chromosome("g"), Chromosome("h"), Chromosome("iI")})
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);
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}
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@ -17,7 +17,6 @@
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#include <tools/yulPhaser/Population.h>
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#include <tools/yulPhaser/Program.h>
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#include <libsolutil/CommonData.h>
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#include <libsolutil/CommonIO.h>
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@ -64,7 +63,7 @@ bool phaser::isFitter(Individual const& a, Individual const& b)
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}
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Population Population::makeRandom(
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Program _program,
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shared_ptr<FitnessMetric const> _fitnessMetric,
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size_t _size,
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function<size_t()> _chromosomeLengthGenerator
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)
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@ -73,30 +72,23 @@ Population Population::makeRandom(
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for (size_t i = 0; i < _size; ++i)
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individuals.push_back({Chromosome::makeRandom(_chromosomeLengthGenerator())});
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return Population(move(_program), individuals);
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return Population(move(_fitnessMetric), move(individuals));
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}
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Population Population::makeRandom(
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Program _program,
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shared_ptr<FitnessMetric const> _fitnessMetric,
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size_t _size,
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size_t _minChromosomeLength,
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size_t _maxChromosomeLength
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)
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{
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return makeRandom(
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move(_program),
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move(_fitnessMetric),
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_size,
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std::bind(uniformChromosomeLength, _minChromosomeLength, _maxChromosomeLength)
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);
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}
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size_t Population::measureFitness(Chromosome const& _chromosome, Program const& _program)
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{
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Program programCopy = _program;
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programCopy.optimise(_chromosome.optimisationSteps());
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return programCopy.codeSize();
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}
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void Population::run(optional<size_t> _numRounds, ostream& _outputStream)
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{
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doEvaluation();
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@ -113,16 +105,19 @@ void Population::run(optional<size_t> _numRounds, ostream& _outputStream)
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Population operator+(Population _a, Population _b)
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{
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assert(toString(_a.m_program) == toString(_b.m_program));
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// This operator is meant to be used only with populations sharing the same metric (and, to make
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// things simple, "the same" here means the same exact object in memory).
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assert(_a.m_fitnessMetric == _b.m_fitnessMetric);
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return Population(_a.m_program, move(_a.m_individuals) + move(_b.m_individuals));
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return Population(_a.m_fitnessMetric, move(_a.m_individuals) + move(_b.m_individuals));
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}
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bool Population::operator==(Population const& _other) const
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{
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// TODO: Comparing programs is pretty heavy but it's just a stopgap. It will soon be replaced
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// by a comparison of fitness metric associated with the population (once metrics are introduced).
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return m_individuals == _other.m_individuals && toString(m_program) == toString(_other.m_program);
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// We consider populations identical only if they share the same exact instance of the metric.
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// It might be possible to define some notion of equality for metric objects but it would
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// be an overkill since mixing populations using different metrics is not a common use case.
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return m_individuals == _other.m_individuals && m_fitnessMetric == _other.m_fitnessMetric;
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}
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ostream& phaser::operator<<(ostream& _stream, Population const& _population)
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@ -143,7 +138,7 @@ void Population::doEvaluation()
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{
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for (auto& individual: m_individuals)
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if (!individual.fitness.has_value())
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individual.fitness = measureFitness(individual.chromosome, m_program);
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individual.fitness = m_fitnessMetric->evaluate(individual.chromosome);
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}
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void Population::doSelection()
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@ -18,7 +18,7 @@
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#pragma once
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#include <tools/yulPhaser/Chromosome.h>
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#include <tools/yulPhaser/Program.h>
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#include <tools/yulPhaser/FitnessMetrics.h>
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#include <tools/yulPhaser/SimulationRNG.h>
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#include <optional>
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@ -64,28 +64,31 @@ bool isFitter(Individual const& a, Individual const& b);
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* Each round of the algorithm involves mutating existing individuals, evaluating their fitness
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* and selecting the best ones for the next round.
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*
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* An individual is a sequence of optimiser steps represented by a @a Chromosome instance. The whole
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* population is associated with a fixed Yul program. By applying the steps to the @a Program
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* instance the class can compute fitness of the individual.
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* An individual is a sequence of optimiser steps represented by a @a Chromosome instance.
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* Individuals are stored together with a fitness value that can be computed by the fitness metric
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* associated with the population.
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*/
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class Population
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{
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public:
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static constexpr size_t MaxChromosomeLength = 30;
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explicit Population(Program _program, std::vector<Chromosome> _chromosomes = {}):
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explicit Population(
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std::shared_ptr<FitnessMetric const> _fitnessMetric,
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std::vector<Chromosome> _chromosomes = {}
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):
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Population(
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std::move(_program),
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std::move(_fitnessMetric),
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chromosomesToIndividuals(std::move(_chromosomes))
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) {}
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static Population makeRandom(
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Program _program,
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std::shared_ptr<FitnessMetric const> _fitnessMetric,
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size_t _size,
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std::function<size_t()> _chromosomeLengthGenerator
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);
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static Population makeRandom(
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Program _program,
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std::shared_ptr<FitnessMetric const> _fitnessMetric,
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size_t _size,
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size_t _minChromosomeLength,
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size_t _maxChromosomeLength
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@ -94,11 +97,11 @@ public:
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void run(std::optional<size_t> _numRounds, std::ostream& _outputStream);
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friend Population (::operator+)(Population _a, Population _b);
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std::shared_ptr<FitnessMetric const> fitnessMetric() const { return m_fitnessMetric; }
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std::vector<Individual> const& individuals() const { return m_individuals; }
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static size_t uniformChromosomeLength(size_t _min, size_t _max) { return SimulationRNG::uniformInt(_min, _max); }
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static size_t binomialChromosomeLength(size_t _max) { return SimulationRNG::binomialInt(_max, 0.5); }
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static size_t measureFitness(Chromosome const& _chromosome, Program const& _program);
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bool operator==(Population const& _other) const;
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bool operator!=(Population const& _other) const { return !(*this == _other); }
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@ -106,8 +109,8 @@ public:
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friend std::ostream& operator<<(std::ostream& _stream, Population const& _population);
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private:
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explicit Population(Program _program, std::vector<Individual> _individuals):
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m_program{std::move(_program)},
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explicit Population(std::shared_ptr<FitnessMetric const> _fitnessMetric, std::vector<Individual> _individuals):
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m_fitnessMetric(std::move(_fitnessMetric)),
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m_individuals{std::move(_individuals)} {}
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void doMutation();
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@ -123,7 +126,7 @@ private:
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);
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static std::vector<Individual> sortedIndividuals(std::vector<Individual> _individuals);
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Program m_program;
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std::shared_ptr<FitnessMetric const> m_fitnessMetric;
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std::vector<Individual> m_individuals;
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};
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@ -17,6 +17,7 @@
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#include <tools/yulPhaser/Exceptions.h>
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#include <tools/yulPhaser/Population.h>
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#include <tools/yulPhaser/FitnessMetrics.h>
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#include <tools/yulPhaser/Program.h>
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#include <tools/yulPhaser/SimulationRNG.h>
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@ -71,8 +72,9 @@ CharStream loadSource(string const& _sourcePath)
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void runAlgorithm(string const& _sourcePath)
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{
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CharStream sourceCode = loadSource(_sourcePath);
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shared_ptr<FitnessMetric> fitnessMetric = make_shared<ProgramSize>(Program::load(sourceCode));
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auto population = Population::makeRandom(
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Program::load(sourceCode),
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fitnessMetric,
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10,
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bind(Population::binomialChromosomeLength, Population::MaxChromosomeLength)
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);
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