/// <summary> /// Trims the genomeList in each specie back to the number of elite genomes specified in /// specieStatsArr. Returns true if there are empty species following trimming. /// </summary> private bool TrimSpeciesBackToElite(SpecieStats[] specieStatsArr) { bool emptySpeciesFlag = false; int count = _specieList.Count; for(int i=0; i<count; i++) { Specie<TGenome> specie = _specieList[i]; SpecieStats stats = specieStatsArr[i]; int removeCount = specie.GenomeList.Count - stats._eliteSizeInt; specie.GenomeList.RemoveRange(stats._eliteSizeInt, removeCount); if(0 == stats._eliteSizeInt) { emptySpeciesFlag = true; } } return emptySpeciesFlag; }
/// <summary> /// Create the required number of offspring genomes, using specieStatsArr as the basis for selecting how /// many offspring are produced from each species. /// </summary> private List <TGenome> CreateOffspring(SpecieStats[] specieStatsArr, int offspringCount) { // Build a RouletteWheelLayout for selecting species for cross-species reproduction. // While we're in the loop we also pre-build a RouletteWheelLayout for each specie; // Doing this before the main loop means we have RouletteWheelLayouts available for // all species when performing cross-specie matings. int specieCount = specieStatsArr.Length; double[] specieFitnessArr = new double[specieCount]; RouletteWheelLayout[] rwlArr = new RouletteWheelLayout[specieCount]; // Count of species with non-zero selection size. // If this is exactly 1 then we skip inter-species mating. One is a special case because for 0 the // species all get an even chance of selection, and for >1 we can just select normally. int nonZeroSpecieCount = 0; for (int i = 0; i < specieCount; i++) { // Array of probabilities for specie selection. Note that some of these probabilites can be zero, but at least one of them won't be. SpecieStats inst = specieStatsArr[i]; specieFitnessArr[i] = inst._selectionSizeInt; if (0 != inst._selectionSizeInt) { nonZeroSpecieCount++; } // For each specie we build a RouletteWheelLayout for genome selection within // that specie. Fitter genomes have higher probability of selection. List <TGenome> genomeList = _specieList[i].GenomeList; double[] probabilities = new double[inst._selectionSizeInt]; for (int j = 0; j < inst._selectionSizeInt; j++) { probabilities[j] = genomeList[j].EvaluationInfo.Fitness; } rwlArr[i] = new RouletteWheelLayout(probabilities); } // Complete construction of RouletteWheelLayout for specie selection. RouletteWheelLayout rwlSpecies = new RouletteWheelLayout(specieFitnessArr); // Produce offspring from each specie in turn and store them in offspringList. List <TGenome> offspringList = new List <TGenome>(offspringCount); for (int specieIdx = 0; specieIdx < specieCount; specieIdx++) { SpecieStats inst = specieStatsArr[specieIdx]; List <TGenome> genomeList = _specieList[specieIdx].GenomeList; // Get RouletteWheelLayout for genome selection. RouletteWheelLayout rwl = rwlArr[specieIdx]; // --- Produce the required number of offspring from asexual reproduction. for (int i = 0; i < inst._offspringAsexualCount; i++) { int genomeIdx = RouletteWheel.SingleThrow(rwl, _rng); TGenome offspring = genomeList[genomeIdx].CreateOffspring(_currentGeneration); offspringList.Add(offspring); } _stats._asexualOffspringCount += (ulong)inst._offspringAsexualCount; // --- Produce the required number of offspring from sexual reproduction. // Cross-specie mating. // If nonZeroSpecieCount is exactly 1 then we skip inter-species mating. One is a special case because // for 0 the species all get an even chance of selection, and for >1 we can just select species normally. int crossSpecieMatings = nonZeroSpecieCount == 1 ? 0 : (int)Utilities.ProbabilisticRound(_eaParams.InterspeciesMatingProportion * inst._offspringSexualCount, _rng); _stats._sexualOffspringCount += (ulong)(inst._offspringSexualCount - crossSpecieMatings); _stats._interspeciesOffspringCount += (ulong)crossSpecieMatings; // An index that keeps track of how many offspring have been produced in total. int matingsCount = 0; for (; matingsCount < crossSpecieMatings; matingsCount++) { TGenome offspring = CreateOffspring_CrossSpecieMating(rwl, rwlArr, rwlSpecies, specieIdx, genomeList); offspringList.Add(offspring); } // For the remainder we use normal intra-specie mating. // Test for special case - we only have one genome to select from in the current specie. if (1 == inst._selectionSizeInt) { // Fall-back to asexual reproduction. for (; matingsCount < inst._offspringSexualCount; matingsCount++) { int genomeIdx = RouletteWheel.SingleThrow(rwl, _rng); TGenome offspring = genomeList[genomeIdx].CreateOffspring(_currentGeneration); offspringList.Add(offspring); } } else { // Remainder of matings are normal within-specie. for (; matingsCount < inst._offspringSexualCount; matingsCount++) { // Select parents. SelectRouletteWheelItem() guarantees parent2Idx!=parent1Idx int parent1Idx = RouletteWheel.SingleThrow(rwl, _rng); TGenome parent1 = genomeList[parent1Idx]; // Remove selected parent from set of possible outcomes. RouletteWheelLayout rwlTmp = rwl.RemoveOutcome(parent1Idx); if (0.0 != rwlTmp.ProbabilitiesTotal) { // Get the two parents to mate. int parent2Idx = RouletteWheel.SingleThrow(rwlTmp, _rng); TGenome parent2 = genomeList[parent2Idx]; TGenome offspring = parent1.CreateOffspring(parent2, _currentGeneration); offspringList.Add(offspring); } else { // No other parent has a non-zero selection probability (they all have zero fitness). // Fall back to asexual reproduction of the single genome with a non-zero fitness. TGenome offspring = parent1.CreateOffspring(_currentGeneration); offspringList.Add(offspring); } } } } _stats._totalOffspringCount += (ulong)offspringCount; return(offspringList); }
/// <summary> /// Calculate statistics for each specie. This method is at the heart of the evolutionary algorithm, /// the key things that are achieved in this method are - for each specie we calculate: /// 1) The target size based on fitness of the specie's member genomes. /// 2) The elite size based on the current size. Potentially this could be higher than the target /// size, so a target size is taken to be a hard limit. /// 3) Following (1) and (2) we can calculate the total number offspring that need to be generated /// for the current generation. /// </summary> private SpecieStats[] CalcSpecieStats(out int offspringCount) { double totalMeanFitness = 0.0; // Build stats array and get the mean fitness of each specie. int specieCount = _specieList.Count; SpecieStats[] specieStatsArr = new SpecieStats[specieCount]; for (int i = 0; i < specieCount; i++) { SpecieStats inst = new SpecieStats(); specieStatsArr[i] = inst; inst._meanFitness = _specieList[i].CalcMeanFitness(); totalMeanFitness += inst._meanFitness; } // Calculate the new target size of each specie using fitness sharing. // Keep a total of all allocated target sizes, typically this will vary slightly from the // overall target population size due to rounding of each real/fractional target size. int totalTargetSizeInt = 0; if (0.0 == totalMeanFitness) { // Handle specific case where all genomes/species have a zero fitness. // Assign all species an equal targetSize. double targetSizeReal = (double)_populationSize / (double)specieCount; for (int i = 0; i < specieCount; i++) { SpecieStats inst = specieStatsArr[i]; inst._targetSizeReal = targetSizeReal; // Stochastic rounding will result in equal allocation if targetSizeReal is a whole // number, otherwise it will help to distribute allocations evenly. inst._targetSizeInt = (int)Utilities.ProbabilisticRound(targetSizeReal, _rng); // Total up discretized target sizes. totalTargetSizeInt += inst._targetSizeInt; } } else { // The size of each specie is based on its fitness relative to the other species. for (int i = 0; i < specieCount; i++) { SpecieStats inst = specieStatsArr[i]; inst._targetSizeReal = (inst._meanFitness / totalMeanFitness) * (double)_populationSize; // Discretize targetSize (stochastic rounding). inst._targetSizeInt = (int)Utilities.ProbabilisticRound(inst._targetSizeReal, _rng); // Total up discretized target sizes. totalTargetSizeInt += inst._targetSizeInt; } } // Discretized target sizes may total up to a value that is not equal to the required overall population // size. Here we check this and if there is a difference then we adjust the specie's targetSizeInt values // to compensate for the difference. // // E.g. If we are short of the required populationSize then we add the required additional allocation to // selected species based on the difference between each specie's targetSizeReal and targetSizeInt values. // What we're effectively doing here is assigning the additional required target allocation to species based // on their real target size in relation to their actual (integer) target size. // Those species that have an actual allocation below there real allocation (the difference will often // be a fractional amount) will be assigned extra allocation probabilistically, where the probability is // based on the differences between real and actual target values. // // Where the actual target allocation is higher than the required target (due to rounding up), we use the same // method but we adjust specie target sizes down rather than up. int targetSizeDeltaInt = totalTargetSizeInt - _populationSize; if (targetSizeDeltaInt < 0) { // Check for special case. If we are short by just 1 then increment targetSizeInt for the specie containing // the best genome. We always ensure that this specie has a minimum target size of 1 with a final test (below), // by incrementing here we avoid the probabilistic allocation below followed by a further correction if // the champ specie ended up with a zero target size. if (-1 == targetSizeDeltaInt) { specieStatsArr[_bestSpecieIdx]._targetSizeInt++; } else { // We are short of the required populationSize. Add the required additional allocations. // Determine each specie's relative probability of receiving additional allocation. double[] probabilities = new double[specieCount]; for (int i = 0; i < specieCount; i++) { SpecieStats inst = specieStatsArr[i]; probabilities[i] = Math.Max(0.0, inst._targetSizeReal - (double)inst._targetSizeInt); } // Use a built in class for choosing an item based on a list of relative probabilities. RouletteWheelLayout rwl = new RouletteWheelLayout(probabilities); // Probabilistically assign the required number of additional allocations. // ENHANCEMENT: We can improve the allocation fairness by updating the RouletteWheelLayout // after each allocation (to reflect that allocation). // targetSizeDeltaInt is negative, so flip the sign for code clarity. targetSizeDeltaInt *= -1; for (int i = 0; i < targetSizeDeltaInt; i++) { int specieIdx = RouletteWheel.SingleThrow(rwl, _rng); specieStatsArr[specieIdx]._targetSizeInt++; } } } else if (targetSizeDeltaInt > 0) { // We have overshot the required populationSize. Adjust target sizes down to compensate. // Determine each specie's relative probability of target size downward adjustment. double[] probabilities = new double[specieCount]; for (int i = 0; i < specieCount; i++) { SpecieStats inst = specieStatsArr[i]; probabilities[i] = Math.Max(0.0, (double)inst._targetSizeInt - inst._targetSizeReal); } // Use a built in class for choosing an item based on a list of relative probabilities. RouletteWheelLayout rwl = new RouletteWheelLayout(probabilities); // Probabilistically decrement specie target sizes. // ENHANCEMENT: We can improve the selection fairness by updating the RouletteWheelLayout // after each decrement (to reflect that decrement). for (int i = 0; i < targetSizeDeltaInt;) { int specieIdx = RouletteWheel.SingleThrow(rwl, _rng); // Skip empty species. This can happen because the same species can be selected more than once. if (0 != specieStatsArr[specieIdx]._targetSizeInt) { specieStatsArr[specieIdx]._targetSizeInt--; i++; } } } // We now have Sum(_targetSizeInt) == _populationSize. Debug.Assert(SumTargetSizeInt(specieStatsArr) == _populationSize); // TODO: Better way of ensuring champ species has non-zero target size? // However we need to check that the specie with the best genome has a non-zero targetSizeInt in order // to ensure that the best genome is preserved. A zero size may have been allocated in some pathological cases. if (0 == specieStatsArr[_bestSpecieIdx]._targetSizeInt) { specieStatsArr[_bestSpecieIdx]._targetSizeInt++; // Adjust down the target size of one of the other species to compensate. // Pick a specie at random (but not the champ specie). Note that this may result in a specie with a zero // target size, this is OK at this stage. We handle allocations of zero in PerformOneGeneration(). int idx = RouletteWheel.SingleThrowEven(specieCount - 1, _rng); idx = idx == _bestSpecieIdx ? idx + 1 : idx; if (specieStatsArr[idx]._targetSizeInt > 0) { specieStatsArr[idx]._targetSizeInt--; } else { // Scan forward from this specie to find a suitable one. bool done = false; idx++; for (; idx < specieCount; idx++) { if (idx != _bestSpecieIdx && specieStatsArr[idx]._targetSizeInt > 0) { specieStatsArr[idx]._targetSizeInt--; done = true; break; } } // Scan forward from start of species list. if (!done) { for (int i = 0; i < specieCount; i++) { if (i != _bestSpecieIdx && specieStatsArr[i]._targetSizeInt > 0) { specieStatsArr[i]._targetSizeInt--; done = true; break; } } if (!done) { throw new SharpNeatException("CalcSpecieStats(). Error adjusting target population size down. Is the population size less than or equal to the number of species?"); } } } } // Now determine the eliteSize for each specie. This is the number of genomes that will remain in a // specie from the current generation and is a proportion of the specie's current size. // Also here we calculate the total number of offspring that will need to be generated. offspringCount = 0; for (int i = 0; i < specieCount; i++) { // Special case - zero target size. if (0 == specieStatsArr[i]._targetSizeInt) { specieStatsArr[i]._eliteSizeInt = 0; continue; } // Discretize the real size with a probabilistic handling of the fractional part. double eliteSizeReal = _specieList[i].GenomeList.Count * _eaParams.ElitismProportion; int eliteSizeInt = (int)Utilities.ProbabilisticRound(eliteSizeReal, _rng); // Ensure eliteSizeInt is no larger than the current target size (remember it was calculated // against the current size of the specie not its new target size). SpecieStats inst = specieStatsArr[i]; inst._eliteSizeInt = Math.Min(eliteSizeInt, inst._targetSizeInt); // Ensure the champ specie preserves the champ genome. We do this even if the targetsize is just 1 // - which means the champ genome will remain and no offspring will be produced from it, apart from // the (usually small) chance of a cross-species mating. if (i == _bestSpecieIdx && inst._eliteSizeInt == 0) { Debug.Assert(inst._targetSizeInt != 0, "Zero target size assigned to champ specie."); inst._eliteSizeInt = 1; } // Now we can determine how many offspring to produce for the specie. inst._offspringCount = inst._targetSizeInt - inst._eliteSizeInt; offspringCount += inst._offspringCount; // While we're here we determine the split between asexual and sexual reproduction. Again using // some probabilistic logic to compensate for any rounding bias. double offspringAsexualCountReal = (double)inst._offspringCount * _eaParams.OffspringAsexualProportion; inst._offspringAsexualCount = (int)Utilities.ProbabilisticRound(offspringAsexualCountReal, _rng); inst._offspringSexualCount = inst._offspringCount - inst._offspringAsexualCount; // Also while we're here we calculate the selectionSize. The number of the specie's fittest genomes // that are selected from to create offspring. This should always be at least 1. double selectionSizeReal = _specieList[i].GenomeList.Count * _eaParams.SelectionProportion; inst._selectionSizeInt = Math.Max(1, (int)Utilities.ProbabilisticRound(selectionSizeReal, _rng)); } return(specieStatsArr); }