/// <summary>
/// Construct with default set of parameters.
/// </summary>
public NeatGenomeParameters()
{
// Previous default option:
// _normalNeuronActivFn = SteepenedSigmoid.__DefaultInstance;
_normalNeuronActivFn = InverseAbsoluteSteepSigmoid.__DefaultInstance;
_regulatoryActivFn = StepFunctionOffset.__DefaultInstance;
_outputNeuronActivFn = Proportional.__DefaultInstance;
_connectionWeightRange = DefaultConnectionWeightRange;
_initialInterconnectionsProportion = DefaultInitialInterconnectionsProportion;
_disjointExcessGenesRecombineProbability = DefaultDisjointExcessGenesRecombineProbability;
_connectionWeightMutationProbability = DefaultConnectionWeightMutationProbability;
_addNodeMutationProbability = DefaultAddNodeMutationProbability;
_addConnectionMutationProbability = DefaultAddConnectionMutationProbability;
_nodeAuxStateMutationProbability = DefaultNodeAuxStateMutationProbability;
_deleteConnectionMutationProbability = DefaultDeleteConnectionMutationProbability;
_rouletteWheelLayout = CreateRouletteWheelLayout();
_rouletteWheelLayoutNonDestructive = CreateRouletteWheelLayout_NonDestructive();
// Create a connection weight mutation scheme.
_connectionMutationInfoList = CreateConnectionWeightMutationScheme_Default();
// No fitness history.
_fitnessHistoryLength = 0;
}
static Dictionary <string, PasswordInfo> morphEnglish(Dictionary <string, PasswordInfo> english, double[] digitProbs, double[] posProbs, int minLength = 0)
{
Dictionary <string, PasswordInfo> results = new Dictionary <string, PasswordInfo>();
FastRandom random = new FastRandom();
Console.WriteLine("Probs sum: {0}", digitProbs.Sum());
RouletteWheelLayout digitLayout = new RouletteWheelLayout(digitProbs);
RouletteWheelLayout posLayout = new RouletteWheelLayout(posProbs);
int alreadyNumbered = 0;
foreach (string s in english.Keys)
{
bool numbered = false;
for (int i = 0; i < s.Length; i++)
{
if (s[i] >= '0' && s[i] <= '9')
{
alreadyNumbered++;
numbered = true;
break;
}
}
string morphedPassword = s;
while (!numbered || morphedPassword.Length < minLength)
{
int toAdd = RouletteWheel.SingleThrow(digitLayout, random);
int pos = RouletteWheel.SingleThrow(posLayout, random);
if (pos == 0)
{
break;
}
else if (pos == 1)
{
morphedPassword = toAdd + morphedPassword;
}
else if (pos == 2)
{
morphedPassword = morphedPassword + toAdd;
}
else
{
pos = random.Next(morphedPassword.Length);
morphedPassword = morphedPassword.Substring(0, pos) + toAdd + morphedPassword.Substring(pos, morphedPassword.Length - pos);
}
numbered = true;
}
PasswordInfo val;
if (!results.TryGetValue(morphedPassword, out val))
{
results.Add(morphedPassword, new PasswordInfo(1, 1));
}
}
Console.WriteLine("Had numbers already: {0}", alreadyNumbered);
return(results);
}
/// <summary>
/// Constructs an activation function library with a default set of activation functions.
/// </summary>
public DefaultCppnActivationFunctionLibrary()
{
_functionList = new List <ActivationFunctionInfo>(4);
double[] probabilities = { 0.25, 0.25, 0.25, 0.25 };
_functionList.Add(new ActivationFunctionInfo(0, probabilities[0], Linear.__DefaultInstance));
_functionList.Add(new ActivationFunctionInfo(1, probabilities[1], BipolarSigmoid.__DefaultInstance));
_functionList.Add(new ActivationFunctionInfo(2, probabilities[2], Gaussian.__DefaultInstance));
_functionList.Add(new ActivationFunctionInfo(3, probabilities[3], Sine.__DefaultInstance));
_rwl = new RouletteWheelLayout(probabilities);
_functionDict = CreateFunctionDictionary(_functionList);
}
public MarkovChain(MarkovChainNode[] nodes, int stepsPerActivation, FastRandom random)
{
_nodes = nodes;
_stepsPerActivation = stepsPerActivation;
_random = random;
_rouletteWheels = new RouletteWheelLayout[nodes.Length];
for (int i = 0; i < nodes.Length; i++)
{
_rouletteWheels[i] = new RouletteWheelLayout(nodes[i].TransitionProbabilities);
}
}
/// <summary>
/// Constructs an activation function library with the provided list of activation functions.
/// </summary>
public DefaultActivationFunctionLibrary(IList <ActivationFunctionInfo> fnList)
{
// Build a RouletteWheelLayout based on the selection probability on each item.
int count = fnList.Count;
double[] probabilities = new double[count];
for (int i = 0; i < count; i++)
{
probabilities[i] = fnList[i].SelectionProbability;
}
_rwl = new RouletteWheelLayout(probabilities);
_functionList = fnList;
// Build a dictionary of functions keyed on integer ID.
_functionDict = CreateFunctionDictionary(_functionList);
}
/// <summary>
/// Move the prey. The prey moves by a simple set of stochastic rules that make it more likely to move away from
/// the agent, and moreso when it is close.
/// </summary>
public void MovePrey()
{
// Determine if prey will move in this timestep. (Speed is simulated stochastically)
if (_rng.NextDouble() > _preySpeed)
{
return;
}
// Determine position of agent relative to prey.
PolarPoint relPolarPos = PolarPoint.FromCartesian(_agentPos - _preyPos);
// Calculate probabilities of moving in each of the four directions. This stochastic strategy is taken from:
// Incremental Evolution Of Complex General Behavior, Faustino Gomez and Risto Miikkulainen (1997)
// (http://nn.cs.utexas.edu/downloads/papers/gomez.adaptive-behavior.pdf)
// Essentially the prey moves randomply but we bias the movements so the prey moves away from the agent, and thus
// generally avoids getting eaten through stupidity.
double T = MovePrey_T(relPolarPos.Radial);
double[] probs = new double[4];
probs[0] = Math.Exp((CalcAngleDelta(relPolarPos.Theta, Math.PI / 2.0) / Math.PI) * T * 0.33); // North.
probs[1] = Math.Exp((CalcAngleDelta(relPolarPos.Theta, 0) / Math.PI) * T * 0.33); // East.
probs[2] = Math.Exp((CalcAngleDelta(relPolarPos.Theta, Math.PI * 1.5) / Math.PI) * T * 0.33); // South.
probs[3] = Math.Exp((CalcAngleDelta(relPolarPos.Theta, Math.PI) / Math.PI) * T * 0.33); // West.
RouletteWheelLayout rwl = new RouletteWheelLayout(probs);
int action = RouletteWheel.SingleThrow(rwl, _rng);
switch (action)
{
case 0: // Move north.
_preyPos._y = Math.Min(_preyPos._y + 1, _gridSize - 1);
break;
case 1: // Move east.
_preyPos._x = Math.Min(_preyPos._x + 1, _gridSize - 1);
break;
case 2: // Move south.
_preyPos._y = Math.Max(_preyPos._y - 1, 0);
break;
case 3: // Move west (is the best?)
_preyPos._x = Math.Max(_preyPos._x - 1, 0);
break;
}
}
/// <summary>
/// Copy constructor.
/// </summary>
public NeatGenomeParameters(NeatGenomeParameters copyFrom)
{
_feedforwardOnly = copyFrom._feedforwardOnly;
_activationFn = copyFrom._activationFn;
_connectionWeightRange = copyFrom._connectionWeightRange;
_initialInterconnectionsProportion = copyFrom._initialInterconnectionsProportion;
_disjointExcessGenesRecombineProbability = copyFrom._disjointExcessGenesRecombineProbability;
_connectionWeightMutationProbability = copyFrom._connectionWeightMutationProbability;
_addNodeMutationProbability = copyFrom._addNodeMutationProbability;
_addConnectionMutationProbability = copyFrom._addConnectionMutationProbability;
_nodeAuxStateMutationProbability = copyFrom._nodeAuxStateMutationProbability;
_deleteConnectionMutationProbability = copyFrom._deleteConnectionMutationProbability;
_rouletteWheelLayout = new RouletteWheelLayout(copyFrom._rouletteWheelLayout);
_rouletteWheelLayoutNonDestructive = new RouletteWheelLayout(copyFrom._rouletteWheelLayoutNonDestructive);
_connectionMutationInfoList = new ConnectionMutationInfoList(copyFrom._connectionMutationInfoList);
_connectionMutationInfoList.Initialize();
_fitnessHistoryLength = copyFrom._fitnessHistoryLength;
}
/// <summary>
/// Construct with default set of parameters.
/// </summary>
public NeatGenomeParameters()
{
_activationFn = SteepenedSigmoid.__DefaultInstance;
_connectionWeightRange = DefaultConnectionWeightRange;
_initialInterconnectionsProportion = DefaultInitialInterconnectionsProportion;
_disjointExcessGenesRecombineProbability = DefaultDisjointExcessGenesRecombineProbability;
_connectionWeightMutationProbability = DefaultConnectionWeightMutationProbability;
_addNodeMutationProbability = DefaultAddNodeMutationProbability;
_addConnectionMutationProbability = DefaultAddConnectionMutationProbability;
_nodeAuxStateMutationProbability = DefaultNodeAuxStateMutationProbability;
_deleteConnectionMutationProbability = DefaultDeleteConnectionMutationProbability;
_rouletteWheelLayout = CreateRouletteWheelLayout();
_rouletteWheelLayoutNonDestructive = CreateRouletteWheelLayout_NonDestructive();
// Create a connection weight mutation scheme.
_connectionMutationInfoList = CreateConnectionWeightMutationScheme_Default();
// No fitness history.
_fitnessHistoryLength = 0;
}
/// <summary>
/// Cross specie mating.
/// </summary>
/// <param name="rwl">RouletteWheelLayout for selectign genomes in teh current specie.</param>
/// <param name="rwlArr">Array of RouletteWheelLayout objects for genome selection. One for each specie.</param>
/// <param name="rwlSpecies">RouletteWheelLayout for selecting species. Based on relative fitness of species.</param>
/// <param name="currentSpecieIdx">Current specie's index in _specieList</param>
/// <param name="genomeList">Current specie's genome list.</param>
private TGenome CreateOffspring_CrossSpecieMating(RouletteWheelLayout rwl,
RouletteWheelLayout[] rwlArr,
RouletteWheelLayout rwlSpecies,
int currentSpecieIdx,
IList <TGenome> genomeList)
{
// Select parent from current specie.
int parent1Idx = RouletteWheel.SingleThrow(rwl, _rng);
// Select specie other than current one for 2nd parent genome.
RouletteWheelLayout rwlSpeciesTmp = rwlSpecies.RemoveOutcome(currentSpecieIdx);
int specie2Idx = RouletteWheel.SingleThrow(rwlSpeciesTmp, _rng);
// Select a parent genome from the second specie.
int parent2Idx = RouletteWheel.SingleThrow(rwlArr[specie2Idx], _rng);
// Get the two parents to mate.
TGenome parent1 = genomeList[parent1Idx];
TGenome parent2 = _specieList[specie2Idx].GenomeList[parent2Idx];
return(parent1.CreateOffspring(parent2, _currentGeneration));
}
/// <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);
}
private void produceOffspring()
{
double[] offspringCount = new double[LEEAParams.SPECIESCOUNT];
// generate species-specific genome lists
List <QEAGenome>[] speciesGenomes = new List <QEAGenome> [LEEAParams.SPECIESCOUNT];
for (int i = 0; i < LEEAParams.SPECIESCOUNT; i++)
{
speciesGenomes[i] = new List <QEAGenome>();
}
for (int i = 0; i < genomeList.Count; i++)
{
speciesGenomes[genomeList[i].Species].Add(genomeList[i]);
}
// determine offspring count for each species
if (LEEAParams.SPECIESCOUNT == 1)
{
offspringCount[0] = LEEAParams.POPSIZE;
}
else
{
double[] specieFitness = new double[LEEAParams.SPECIESCOUNT];
// calculate species stats to determine how many offspring each species is granted
for (int i = 0; i < LEEAParams.SPECIESCOUNT; i++)
{
for (int j = 0; j < speciesGenomes[i].Count; j++)
{
specieFitness[i] += speciesGenomes[i][j].Fitness;
}
if (speciesGenomes[i].Count != 0)
{
specieFitness[i] /= speciesGenomes[i].Count;
}
}
for (int i = 0; i < LEEAParams.SPECIESCOUNT; i++)
{
offspringCount[i] = Math.Round(LEEAParams.POPSIZE * specieFitness[i] / specieFitness.Sum());
}
// rounding error could leave us a few short or long of the pop size, trim or fill to reach population size
RouletteWheelLayout rwl = new RouletteWheelLayout(offspringCount);
while (offspringCount.Sum() > LEEAParams.POPSIZE)
{
int index = RouletteWheel.SingleThrow(rwl, r);
if (offspringCount[index] > 1)
{
offspringCount[index]--;
}
}
while (offspringCount.Sum() < LEEAParams.POPSIZE)
{
int index = RouletteWheel.SingleThrow(rwl, r);
offspringCount[index]++;
}
}
List <QEAGenome> newGeneration = new List <QEAGenome>();
// generate offspring for each species
// parallelism doesn't work if speciescount = 1 here!
for (int i = 0; i < LEEAParams.SPECIESCOUNT; i++)
{
if (offspringCount[i] > 0)
{
// sort the genome list by fitness
Comparison <QEAGenome> comparison = (x, y) => y.Fitness.CompareTo(x.Fitness);
speciesGenomes[i].Sort(comparison);
// determine the top X individuals that we will select from
int selectionNumber = (int)(speciesGenomes[i].Count * LEEAParams.SELECTIONPROPORTION);
if (selectionNumber == 0)
{
selectionNumber = 1;
}
// build list of probabilities based on fitness
double[] probabilities = new double[selectionNumber];
for (int j = 0; j < probabilities.Length; j++)
{
probabilities[j] = speciesGenomes[i][j].Fitness;
}
RouletteWheelLayout rw = new RouletteWheelLayout(probabilities);
// build a list of matings to be performed. This must be done outside of the parallelized section.
int[][] matings = new int[(int)offspringCount[i]][];
for (int j = 0; j < matings.Length; j++)
{
matings[j] = new int[2];
// select main parent
int index = RouletteWheel.SingleThrow(rw, r);
if (r.NextDouble() < LEEAParams.SEXPROPORTION && probabilities.Length > 1) // can't have sexual reproduction if this species only has a single member
{
matings[j][0] = index;
int parent2 = index;
while (parent2 == index)
{
parent2 = RouletteWheel.SingleThrow(rw, r);
}
matings[j][1] = parent2;
}
else
{
matings[j][0] = index;
matings[j][1] = int.MinValue;
}
}
Parallel.For(0, matings.Length, po, j =>
//for (int j = 0; j < matings.Length; j++)
{
// mutate
QEAGenome child;
if (matings[j][1] > int.MinValue)
{
// sexual reproduction
child = speciesGenomes[i][matings[j][0]].createOffspring(speciesGenomes[i][matings[j][1]]);
child.Fitness = (speciesGenomes[i][matings[j][0]].Fitness + speciesGenomes[i][matings[j][1]].Fitness) / 2;
}
else
{
child = speciesGenomes[i][matings[j][0]].createOffspring();
child.Fitness = speciesGenomes[i][matings[j][0]].Fitness;
}
lock (newGeneration)
{
newGeneration.Add(child);
}
});
}
}
// encourage the garbage collector to free up some memory
foreach (QEAGenome g in genomeList)
{
g.weights = null;
}
genomeList = null;
genomeList = newGeneration;
}
/// <summary>
/// Initialize the list. Call this after all items have been aded to the list. This
/// creates a RouletteWheelLayout based upon the activation probability of each item
/// in the list.
/// </summary>
public void Initialize()
{
_rouletteWheelLayout = CreateRouletteWheelLayout();
}
