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https://github.com/ethereum/solidity
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[yul-phaser] Add GenerationalElitistWithExclusivePools algorithm
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@ -29,6 +29,7 @@
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#include <boost/test/unit_test.hpp>
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#include <boost/test/unit_test.hpp>
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#include <boost/test/tools/output_test_stream.hpp>
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#include <boost/test/tools/output_test_stream.hpp>
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#include <algorithm>
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#include <vector>
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#include <vector>
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using namespace std;
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using namespace std;
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@ -132,6 +133,107 @@ BOOST_FIXTURE_TEST_CASE(runNextRound_should_not_replace_any_chromosomes_if_whole
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BOOST_TEST((chromosomeLengths(algorithm.population()) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
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BOOST_TEST((chromosomeLengths(algorithm.population()) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
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}
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}
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE(GenerationalElitistWithExclusivePoolsTest)
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_preserve_elite_and_regenerate_rest_of_population, GeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 6, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
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GenerationalElitistWithExclusivePools::Options options = {
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/* mutationPoolSize = */ 0.2,
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/* crossoverPoolSize = */ 0.2,
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/* randomisationChance = */ 0.0,
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/* deletionVsAdditionChance = */ 1.0,
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/* percentGenesToRandomise = */ 0.0,
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/* percentGenesToAddOrDelete = */ 1.0,
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};
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GenerationalElitistWithExclusivePools algorithm(population, m_output, options);
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assert((chromosomeLengths(algorithm.population()) == vector<size_t>{3, 3, 3, 3, 3, 3, 5, 5, 5, 5}));
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algorithm.runNextRound();
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BOOST_TEST((chromosomeLengths(algorithm.population()) == vector<size_t>{0, 0, 3, 3, 3, 3, 3, 3, 3, 3}));
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_not_replace_elite_with_worse_individuals, GeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 6, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
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GenerationalElitistWithExclusivePools::Options options = {
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/* mutationPoolSize = */ 0.2,
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/* crossoverPoolSize = */ 0.2,
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/* randomisationChance = */ 0.0,
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/* deletionVsAdditionChance = */ 0.0,
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/* percentGenesToRandomise = */ 0.0,
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/* percentGenesToAddOrDelete = */ 1.0,
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};
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GenerationalElitistWithExclusivePools algorithm(population, m_output, options);
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assert(chromosomeLengths(algorithm.population()) == (vector<size_t>{3, 3, 3, 3, 3, 3, 5, 5, 5, 5}));
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algorithm.runNextRound();
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BOOST_TEST((chromosomeLengths(algorithm.population()) == vector<size_t>{3, 3, 3, 3, 3, 3, 3, 3, 7, 7}));
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_generate_individuals_in_the_crossover_pool_by_mutating_the_elite, GeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 20, 5, 5);
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GenerationalElitistWithExclusivePools::Options options = {
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/* mutationPoolSize = */ 0.8,
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/* crossoverPoolSize = */ 0.0,
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/* randomisationChance = */ 0.5,
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/* deletionVsAdditionChance = */ 0.5,
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/* percentGenesToRandomise = */ 1.0,
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/* percentGenesToAddOrDelete = */ 1.0,
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};
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GenerationalElitistWithExclusivePools algorithm(population, m_output, options);
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SimulationRNG::reset(1);
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algorithm.runNextRound();
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BOOST_TEST((
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chromosomeLengths(algorithm.population()) ==
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vector<size_t>{0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 11, 11, 11}
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));
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_generate_individuals_in_the_crossover_pool_by_crossing_over_the_elite, GeneticAlgorithmFixture)
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{
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auto population = (
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Population(m_fitnessMetric, {Chromosome("aa"), Chromosome("ff")}) +
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Population::makeRandom(m_fitnessMetric, 8, 6, 6)
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);
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GenerationalElitistWithExclusivePools::Options options = {
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/* mutationPoolSize = */ 0.0,
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/* crossoverPoolSize = */ 0.8,
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/* randomisationChance = */ 0.0,
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/* deletionVsAdditionChance = */ 0.0,
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/* percentGenesToRandomise = */ 0.0,
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/* percentGenesToAddOrDelete = */ 0.0,
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};
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GenerationalElitistWithExclusivePools algorithm(population, m_output, options);
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assert((chromosomeLengths(algorithm.population()) == vector<size_t>{2, 2, 6, 6, 6, 6, 6, 6, 6, 6}));
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SimulationRNG::reset(1);
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algorithm.runNextRound();
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vector<Individual> const& newIndividuals = algorithm.population().individuals();
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BOOST_TEST((chromosomeLengths(algorithm.population()) == vector<size_t>{2, 2, 2, 2, 2, 2, 2, 2, 2, 2}));
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for (auto& individual: newIndividuals)
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BOOST_TEST((
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individual.chromosome == Chromosome("aa") ||
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individual.chromosome == Chromosome("af") ||
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individual.chromosome == Chromosome("fa") ||
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individual.chromosome == Chromosome("ff")
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));
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BOOST_TEST(any_of(newIndividuals.begin() + 2, newIndividuals.end(), [](auto& individual){
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return individual.chromosome != Chromosome("aa") && individual.chromosome != Chromosome("ff");
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}));
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}
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE_END()
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@ -16,7 +16,9 @@
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*/
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*/
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#include <tools/yulPhaser/GeneticAlgorithms.h>
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#include <tools/yulPhaser/GeneticAlgorithms.h>
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#include <tools/yulPhaser/Mutations.h>
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#include <tools/yulPhaser/Selections.h>
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#include <tools/yulPhaser/Selections.h>
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#include <tools/yulPhaser/PairSelections.h>
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using namespace std;
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using namespace std;
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using namespace solidity::phaser;
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using namespace solidity::phaser;
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@ -48,3 +50,28 @@ void RandomAlgorithm::runNextRound()
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m_options.maxChromosomeLength
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m_options.maxChromosomeLength
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);
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);
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}
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}
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void GenerationalElitistWithExclusivePools::runNextRound()
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{
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double elitePoolSize = 1.0 - (m_options.mutationPoolSize + m_options.crossoverPoolSize);
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RangeSelection elite(0.0, elitePoolSize);
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m_population =
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m_population.select(elite) +
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m_population.select(elite).mutate(
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RandomSelection(m_options.mutationPoolSize / elitePoolSize),
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alternativeMutations(
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m_options.randomisationChance,
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geneRandomisation(m_options.percentGenesToRandomise),
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alternativeMutations(
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m_options.deletionVsAdditionChance,
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geneDeletion(m_options.percentGenesToAddOrDelete),
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geneAddition(m_options.percentGenesToAddOrDelete)
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)
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)
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) +
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m_population.select(elite).crossover(
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RandomPairSelection(m_options.crossoverPoolSize / elitePoolSize / 2),
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randomPointCrossover()
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);
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}
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@ -112,4 +112,57 @@ private:
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Options m_options;
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Options m_options;
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};
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};
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/**
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* A generational, elitist genetic algorithm that replaces the population by mutating and crossing
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* over chromosomes from the elite.
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*
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* The elite consists of individuals not included in the crossover and mutation pools.
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* The crossover operator used is @a randomPointCrossover. The mutation operator is randomly chosen
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* from three possibilities: @a geneRandomisation, @a geneDeletion or @a geneAddition (with
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* configurable probabilities). Each mutation also has a parameter determining the chance of a gene
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* being affected by it.
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*/
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class GenerationalElitistWithExclusivePools: public GeneticAlgorithm
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{
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public:
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struct Options
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{
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double mutationPoolSize; ///< Percentage of population to regenerate using mutations in each round.
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double crossoverPoolSize; ///< Percentage of population to regenerate using crossover in each round.
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double randomisationChance; ///< The chance of choosing @a geneRandomisation as the mutation to perform
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double deletionVsAdditionChance; ///< The chance of choosing @a geneDeletion as the mutation if randomisation was not chosen.
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double percentGenesToRandomise; ///< The chance of any given gene being mutated in gene randomisation.
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double percentGenesToAddOrDelete; ///< The chance of a gene being added (or deleted) in gene addition (or deletion).
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bool isValid() const
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{
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return (
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0 <= mutationPoolSize && mutationPoolSize <= 1.0 &&
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0 <= crossoverPoolSize && crossoverPoolSize <= 1.0 &&
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0 <= randomisationChance && randomisationChance <= 1.0 &&
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0 <= deletionVsAdditionChance && deletionVsAdditionChance <= 1.0 &&
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0 <= percentGenesToRandomise && percentGenesToRandomise <= 1.0 &&
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0 <= percentGenesToAddOrDelete && percentGenesToAddOrDelete <= 1.0 &&
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mutationPoolSize + crossoverPoolSize <= 1.0
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);
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}
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};
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GenerationalElitistWithExclusivePools(
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Population _initialPopulation,
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std::ostream& _outputStream,
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Options const& _options
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):
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GeneticAlgorithm(_initialPopulation, _outputStream),
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m_options(_options)
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{
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assert(_options.isValid());
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}
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void runNextRound() override;
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private:
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Options m_options;
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};
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}
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}
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