private static void AllocateEliteSelectionOffspringCounts(
Species <T> species,
NeatEvolutionAlgorithmSettings eaSettings,
bool isBestGenomeSpecies,
IRandomSource rng)
{
SpeciesStats stats = species.Stats;
// Special case - zero target size.
if (stats.TargetSizeInt == 0)
{
stats.EliteSizeInt = 0;
stats.OffspringCount = 0;
stats.OffspringAsexualCount = 0;
stats.OffspringSexualCount = 0;
stats.SelectionSizeInt = 0;
return;
}
// Calculate the elite size as a proportion of the current species size.
// Note. We discretize the real size with a probabilistic handling of the fractional part.
double eliteSizeReal = species.GenomeList.Count * eaSettings.ElitismProportion;
int eliteSizeInt = (int)NumericsUtils.ProbabilisticRound(eliteSizeReal, rng);
// Ensure eliteSizeInt is no larger than the current target size. (I.e. the value was
// calculated as a proportion of the current size, not the new target size).
stats.EliteSizeInt = Math.Min(eliteSizeInt, stats.TargetSizeInt);
// Special case: ensure the species with the best genome preserves that genome.
// Note. This is done even for a target size of one, which would mean that no offspring are
// produced from the best genome, apart from the (usually small) chance of a cross-species mating.
if (isBestGenomeSpecies && stats.EliteSizeInt == 0)
{
Debug.Assert(stats.TargetSizeInt != 0, "Zero target size assigned to specie that contains the best genome.");
stats.EliteSizeInt = 1;
}
// Determine how many offspring to produce for the species.
stats.OffspringCount = stats.TargetSizeInt - stats.EliteSizeInt;
// Determine the split between asexual and sexual reproduction. Again using probabilistic
// rounding to compensate for any rounding bias.
double offspringAsexualCountReal = stats.OffspringCount * eaSettings.OffspringAsexualProportion;
stats.OffspringAsexualCount = (int)NumericsUtils.ProbabilisticRound(offspringAsexualCountReal, rng);
stats.OffspringSexualCount = stats.OffspringCount - stats.OffspringAsexualCount;
// Calculate the selectionSize. The number of the species' fittest genomes that are selected from
// to create offspring.
// We ensure this is at least one; if TargetSizeInt is zero then it doesn't matter because no genomes will be
// selected from this species to produce offspring, except for cross-species mating, hence the minimum of one is
// a useful general approach.
double selectionSizeReal = species.GenomeList.Count * eaSettings.SelectionProportion;
stats.SelectionSizeInt = Math.Max(1, (int)NumericsUtils.ProbabilisticRound(selectionSizeReal, rng));
}
/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="copyFrom">The settings object to copy.</param>
public NeatEvolutionAlgorithmSettings(NeatEvolutionAlgorithmSettings copyFrom)
{
this.SpeciesCount = copyFrom.SpeciesCount;
this.ElitismProportion = copyFrom.ElitismProportion;
this.SelectionProportion = copyFrom.SelectionProportion;
this.OffspringAsexualProportion = copyFrom.OffspringAsexualProportion;
this.OffspringSexualProportion = copyFrom.OffspringSexualProportion;
this.InterspeciesMatingProportion = copyFrom.InterspeciesMatingProportion;
this.StatisticsMovingAverageHistoryLength = copyFrom.StatisticsMovingAverageHistoryLength;
}
/// <summary>
/// Construct a new instance.
/// </summary>
/// <param name="eaSettings">NEAT evolution algorithm settings.</param>
/// <param name="evaluator">An evaluator of lists of genomes.</param>
/// <param name="speciationStrategy">Speciation strategy.</param>
/// <param name="population">An initial population of genomes.</param>
/// <param name="complexityRegulationStrategy">Complexity regulation strategy.</param>
/// <param name="reproductionAsexualSettings">Asexual reproduction settings.</param>
/// <param name="reproductionSexualSettings">Sexual reproduction settings.</param>
/// <param name="weightMutationScheme">Connection weight mutation scheme.</param>
/// <param name="rng">Random source.</param>
public NeatEvolutionAlgorithm(
NeatEvolutionAlgorithmSettings eaSettings,
IGenomeListEvaluator <NeatGenome <T> > evaluator,
ISpeciationStrategy <NeatGenome <T>, T> speciationStrategy,
NeatPopulation <T> population,
IComplexityRegulationStrategy complexityRegulationStrategy,
NeatReproductionAsexualSettings reproductionAsexualSettings,
NeatReproductionSexualSettings reproductionSexualSettings,
WeightMutationScheme <T> weightMutationScheme,
IRandomSource rng)
{
_eaSettingsCurrent = eaSettings ?? throw new ArgumentNullException(nameof(eaSettings));
_eaSettingsComplexifying = eaSettings;
_eaSettingsSimplifying = eaSettings.CreateSimplifyingSettings();
_evaluator = evaluator ?? throw new ArgumentNullException(nameof(evaluator));
_speciationStrategy = speciationStrategy ?? throw new ArgumentNullException(nameof(speciationStrategy));
_pop = population ?? throw new ArgumentNullException(nameof(population));
_complexityRegulationStrategy = complexityRegulationStrategy ?? throw new ArgumentNullException(nameof(complexityRegulationStrategy));
if (reproductionAsexualSettings == null)
{
throw new ArgumentNullException(nameof(reproductionAsexualSettings));
}
if (reproductionSexualSettings == null)
{
throw new ArgumentNullException(nameof(reproductionSexualSettings));
}
_rng = rng;
_genomeComparerDescending = new GenomeComparerDescending(evaluator.FitnessComparer);
if (eaSettings.SpeciesCount > population.PopulationSize)
{
throw new ArgumentException("Species count is higher then the population size.");
}
_generationSeq = new Int32Sequence();
_reproductionAsexual = new NeatReproductionAsexual <T>(
_pop.MetaNeatGenome, _pop.GenomeBuilder,
_pop.GenomeIdSeq, population.InnovationIdSeq, _generationSeq,
_pop.AddedNodeBuffer, reproductionAsexualSettings, weightMutationScheme);
_reproductionSexual = new NeatReproductionSexual <T>(
_pop.MetaNeatGenome, _pop.GenomeBuilder,
_pop.GenomeIdSeq, _generationSeq,
reproductionSexualSettings);
_offspringBuilder = new OffspringBuilder <T>(
_reproductionAsexual,
_reproductionSexual,
eaSettings.InterspeciesMatingProportion,
evaluator.FitnessComparer);
}
/// <summary>
/// Calculate and update a number of statistical values and target size values on each species in the givben population.
/// </summary>
/// <param name="pop">The population to update species statistics on.</param>
/// <param name="eaSettings">Evolution algorithm settings.</param>
/// <param name="rng">Random source.</param>
public static void CalcAndStoreSpeciesStats(
NeatPopulation <T> pop,
NeatEvolutionAlgorithmSettings eaSettings,
IRandomSource rng)
{
// Calc and store the mean fitness of each species.
CalcAndStoreSpeciesFitnessMeans(pop);
// Calc and store the target size of each species (based on the NEAT fitness sharing method).
SpeciesAllocationCalcs <T> .CalcAndStoreSpeciesAllocationSizes(pop, eaSettings, rng);
}
/// <summary>
/// Calc and store species target sizes based on relative mean fitness of each species, i.e. as per NEAT fitness sharing method.
/// Then calc and store the elite, selection and offspring allocations/counts, per species.
/// </summary>
public static void CalcAndStoreSpeciesAllocationSizes(
NeatPopulation <T> pop,
NeatEvolutionAlgorithmSettings eaSettings,
IRandomSource rng)
{
// Calculate the new target size of each species using fitness sharing.
CalcAndStoreSpeciesTargetSizes(pop, rng);
// Calculate elite, selection and offspring counts, per species.
CalculateAndStoreEliteSelectionOffspringCounts(pop, eaSettings, rng);
}
/// <summary>
/// Creates a new settings object based on the current settings object, but modified to be suitable for use when
/// the evolution algorithm is in simplifying mode.
/// </summary>
/// <returns>A new instance of <see cref="NeatEvolutionAlgorithmSettings"/>.</returns>
public NeatEvolutionAlgorithmSettings CreateSimplifyingSettings()
{
// Clone the current settings object.
var settings = new NeatEvolutionAlgorithmSettings(this)
{
OffspringAsexualProportion = 1.0,
OffspringSexualProportion = 0.0
};
return(settings);
}
/// <summary>
/// Read json into a target instance of <see cref="NeatEvolutionAlgorithmSettings"/>.
/// Settings that are present are read and set on the target settings object; all other settings
/// remain unchanged on the target object.
/// </summary>
/// <param name="target">The target settings object to store the read values on.</param>
/// <param name="jobj">The json object to read from.</param>
public static void Read(
NeatEvolutionAlgorithmSettings target,
JObject jobj)
{
ReadIntOptional(jobj, "speciesCount", x => target.SpeciesCount = x);
ReadDoubleOptional(jobj, "elitismProportion", x => target.ElitismProportion = x);
ReadDoubleOptional(jobj, "selectionProportion", x => target.SelectionProportion = x);
ReadDoubleOptional(jobj, "offspringAsexualProportion", x => target.OffspringAsexualProportion = x);
ReadDoubleOptional(jobj, "offspringSexualProportion", x => target.OffspringSexualProportion = x);
ReadDoubleOptional(jobj, "interspeciesMatingProportion", x => target.InterspeciesMatingProportion = x);
ReadIntOptional(jobj, "statisticsMovingAverageHistoryLength", x => target.StatisticsMovingAverageHistoryLength = x);
}
/// <summary>
/// Construct a new instance.
/// </summary>
/// <param name="eaSettings">NEAT evolution algorithm settings.</param>
/// <param name="evaluator">An evaluator of lists of genomes.</param>
/// <param name="speciationStrategy">Speciation strategy.</param>
/// <param name="population">An initial population of genomes.</param>
/// <param name="reproductionAsexualSettings">Asexual reproduction settings.</param>
/// <param name="reproductionSexualSettings">Sexual reproduction settings.</param>
/// <param name="weightMutationScheme">Connection weight mutation scheme.</param>
public NeatEvolutionAlgorithm(
NeatEvolutionAlgorithmSettings eaSettings,
IGenomeListEvaluator <NeatGenome <T> > evaluator,
ISpeciationStrategy <NeatGenome <T>, T> speciationStrategy,
NeatPopulation <T> population,
NeatReproductionAsexualSettings reproductionAsexualSettings,
NeatReproductionSexualSettings reproductionSexualSettings,
WeightMutationScheme <T> weightMutationScheme)
: this(eaSettings, evaluator, speciationStrategy, population,
reproductionAsexualSettings, reproductionSexualSettings,
weightMutationScheme,
RandomDefaults.CreateRandomSource())
{
}
/// <summary>
/// For each species, allocate the EliteSizeInt, OffspringCount (broken down into OffspringAsexualCount and OffspringSexualCount),
/// and SelectionSizeInt values.
/// </summary>
/// <param name="pop"></param>
/// <param name="eaSettings"></param>
/// <param name="rng"></param>
private static void CalculateAndStoreEliteSelectionOffspringCounts(
NeatPopulation <T> pop,
NeatEvolutionAlgorithmSettings eaSettings,
IRandomSource rng)
{
Species <T>[] speciesArr = pop.SpeciesArray;
// Loop the species, calculating and storing the various size/count properties.
for (int i = 0; i < speciesArr.Length; i++)
{
bool isBestGenomeSpecies = (pop.BestGenomeSpeciesIdx == i);
AllocateEliteSelectionOffspringCounts(speciesArr[i], eaSettings, isBestGenomeSpecies, rng);
}
}
