public override object Read(ES2Reader reader)
{
CrossoverManager data = new CrossoverManager();
Read(reader, data);
return(data);
}
public int currentTrialForEdit = 0; // keeps track of which trial index is currently being edited while in Fitness/Mini-game Panels
// For Each Trial:
// MiniGame Class to store gametype and settings
// Fitness Function Class w/ Fitness component list and settings
// Constructor Functions:
public Player() {
DebugBot.DebugFunctionCall("Player; Player() Constructor!; ", debugFunctionCalls);
InitializeNewPopulation();
masterTrialsList = new List<Trial>();
Trial newTrial = new Trial(this); // make trials list one member long, with a None mini-game value
masterTrialsList.Add(newTrial);
masterCupid = new CrossoverManager();
dataManager = new DataManager(this);
graphKing = new TheGraphKing();
//newTrial.playerRef = this;
}
public int currentTrialForEdit = 0; // keeps track of which trial index is currently being edited while in Fitness/Mini-game Panels
// For Each Trial:
// MiniGame Class to store gametype and settings
// Fitness Function Class w/ Fitness component list and settings
// Constructor Functions:
public Player()
{
DebugBot.DebugFunctionCall("Player; Player() Constructor!; ", debugFunctionCalls);
InitializeNewPopulation();
masterTrialsList = new List <Trial>();
Trial newTrial = new Trial(this); // make trials list one member long, with a None mini-game value
masterTrialsList.Add(newTrial);
masterCupid = new CrossoverManager();
dataManager = new DataManager(this);
graphKing = new TheGraphKing();
//newTrial.playerRef = this;
}
public static Chromosome CreateCrossover(Chromosome ch1, Chromosome ch2)
{
Gene[] genes = CrossoverManager.Crossover(ch1.Genes, ch2.Genes);
var neuralComparableGenes = CrossoverManager.Crossover(ch1.neuralChromosome.GetComparableGenes(), ch2.neuralChromosome.GetComparableGenes());
DendriteGene[] dendriteGenes = new DendriteGene[neuralComparableGenes.Length];
for (int i = 0; i < dendriteGenes.Length; i++)
{
dendriteGenes[i] = neuralComparableGenes[i].GetGene();
}
NeuralChromosome neuralChromosome = new NeuralChromosome(dendriteGenes);
ChromosomeParameters parameters = CrossoverManager.CrossParameters(ch1.Parameters, ch2.Parameters);
Chromosome chromosome = new Chromosome(genes, neuralChromosome, ch1.MutationTracker + "CR", parameters);
return(chromosome);
}
public static bool SameSpecie(Chromosome ch1, Chromosome ch2)
{
KeyValuePair <Chromosome, Chromosome> pair = new KeyValuePair <Chromosome, Chromosome>(ch1, ch2);
if (sameSpecieComputed.ContainsKey(pair))
{
var res = sameSpecieComputed[pair];
if (sameSpecieComputed.Count > 50000)
{
sameSpecieComputed.Clear();
}
return(res);
}
int disjointGenes = CrossoverManager.Difference(ch1.Genes, ch2.Genes);
float differenceValue = 2 * disjointGenes / (float)Math.Max(ch1.Genes.Length, ch2.Genes.Length);
if (differenceValue > Hyperparameters.SPECIES_DIFFERENCE_THRESHOLD)
{
sameSpecieComputed[pair] = false;
return(false);
}
//neural part:
/*
* int disjointNeuralGenes = CrossoverManager.Difference(ch1.neuralChromosome.GetComparableGenes(), ch2.neuralChromosome.GetComparableGenes());
* differenceValue = 2 * disjointNeuralGenes / (float)Math.Max(ch1.neuralChromosome.dendriteGenes.Length, ch2.neuralChromosome.dendriteGenes.Length);
*
* if (differenceValue > 1)
* return false;
*/
sameSpecieComputed[pair] = true;
return(true);
}
public void ApplyTrainingModifierEffects(Trainer trainer)
{
int currentGen = trainer.PlayingCurGeneration;
if (trainer.loadedTrainingSave != null)
{
currentGen += trainer.loadedTrainingSave.endGeneration;
}
//Debug.Log("ApplyTrainingModifierEffects currentGen: " + currentGen.ToString());
CrossoverManager crossoverManager = trainer.PlayerList[0].masterCupid;
int numModifiers = activeTrainingModifierList.Count;
crossoverManager.mutationBlastModifier = 1f;
crossoverManager.bodyMutationBlastModifier = 1f;
if (numModifiers > 0)
{
for (int i = numModifiers - 1; i >= 0; i--)
{
float t = 0f;
switch (activeTrainingModifierList[i].modifierType)
{
case TrainingModifier.TrainingModifierType.LinkExplosion:
// go through all agents and pump them up -- THIS WILL NEED IMPROVEMENTS!!!!
for (int a = 0; a < trainer.PlayerList[0].masterPopulation.masterAgentArray.Length; a++)
{
GenomeNEAT genome = trainer.PlayerList[0].masterPopulation.masterAgentArray[a].brainGenome;
int numNodes = genome.nodeNEATList.Count;
int numNewLinks = (int)((float)numNodes * activeTrainingModifierList[i].linksPerNode);
for (int n = 0; n < numNewLinks; n++)
{
genome.AddNewRandomLink(currentGen);
}
int numLinks = genome.linkNEATList.Count;
int numNewNodes = (int)((float)numLinks * activeTrainingModifierList[i].nodesPerLink);
for (int b = 0; b < numNewNodes; b++)
{
genome.AddNewRandomNode(currentGen, crossoverManager.GetNextAddonInnov());
}
}
activeTrainingModifierList.RemoveAt(i);
break;
case TrainingModifier.TrainingModifierType.MutationBlast:
t = ((float)currentGen - (float)activeTrainingModifierList[i].startGen) / (float)activeTrainingModifierList[i].duration;
crossoverManager = trainer.PlayerList[0].masterCupid;
if (t > 1f)
{
if (activeTrainingModifierList[i].liveForever)
{
t = 1f;
}
else
{
// Duration has expired, and the live forever flag if false, so remove this modifier
crossoverManager.mutationBlastModifier = 1f;
activeTrainingModifierList.RemoveAt(i);
break;
}
}
crossoverManager.mutationBlastModifier = Mathf.Lerp(1f, activeTrainingModifierList[i].minMultiplier, t);
break;
case TrainingModifier.TrainingModifierType.BodyMutationBlast:
t = ((float)currentGen - (float)activeTrainingModifierList[i].startGen) / (float)activeTrainingModifierList[i].duration;
crossoverManager = trainer.PlayerList[0].masterCupid;
if (t > 1f)
{
if (activeTrainingModifierList[i].liveForever)
{
t = 1f;
}
else
{
// Duration has expired, and the live forever flag if false, so remove this modifier
crossoverManager.bodyMutationBlastModifier = 1f;
activeTrainingModifierList.RemoveAt(i);
break;
}
}
crossoverManager.bodyMutationBlastModifier = Mathf.Lerp(1f, activeTrainingModifierList[i].minMultiplier, t);
break;
case TrainingModifier.TrainingModifierType.PruneBrain:
t = ((float)currentGen - (float)activeTrainingModifierList[i].startGen) / (float)activeTrainingModifierList[i].duration;
crossoverManager = trainer.PlayerList[0].masterCupid;
if (t > 1f)
{
t = 1f;
}
t = 1f - t;
crossoverManager.largeBrainPenalty = activeTrainingModifierList[i].largeBrainPenalty * t;
crossoverManager.mutationRemoveLinkChance = activeTrainingModifierList[i].removeLinkChance * t;
crossoverManager.mutationRemoveNodeChance = activeTrainingModifierList[i].removeNodeChance * t;
break;
case TrainingModifier.TrainingModifierType.TargetCone:
break;
case TrainingModifier.TrainingModifierType.TargetForward:
break;
case TrainingModifier.TrainingModifierType.TargetOmni:
break;
case TrainingModifier.TrainingModifierType.VariableTrialTimes:
t = ((float)currentGen - (float)activeTrainingModifierList[i].startGen) / (float)activeTrainingModifierList[i].duration;
Trial trial = trainer.PlayerList[0].masterTrialsList[0];
if (t > 1f)
{
if (activeTrainingModifierList[i].liveForever)
{
t = 1f;
}
else
{
// Remove this modifier
activeTrainingModifierList.RemoveAt(i);
}
}
int minSteps = (int)Mathf.Lerp(activeTrainingModifierList[i].beginMinTime, activeTrainingModifierList[i].endMinTime, t);
int maxSteps = (int)Mathf.Lerp(activeTrainingModifierList[i].beginMaxTime, activeTrainingModifierList[i].endMaxTime, t);
trial.maxEvaluationTimeSteps = maxSteps; // (int)UnityEngine.Random.Range(minSteps, maxSteps);
trial.minEvaluationTimeSteps = minSteps;
break;
case TrainingModifier.TrainingModifierType.WideSearch:
t = ((float)currentGen - (float)activeTrainingModifierList[i].startGen) / (float)activeTrainingModifierList[i].duration;
crossoverManager = trainer.PlayerList[0].masterCupid;
if (t > 1f)
{
t = 1f;
}
t = 1f - t;
//crossoverManager.largeBrainPenalty = activeTrainingModifierList[i].largeBrainPenalty * t;
//crossoverManager.mutationRemoveLinkChance = activeTrainingModifierList[i].removeLinkChance * t;
//crossoverManager.mutationRemoveNodeChance = activeTrainingModifierList[i].removeNodeChance * t;
break;
default:
Debug.Log("Modifier type not found!!! SWITCH DEFAULT CASE");
break;
}
}
}
}
public void InitializePanelWithTrainerData() {
DebugBot.DebugFunctionCall("TCrossoverUI; InitializePanelWithTrainerData(); ", debugFunctionCalls);
//CheckActivationCriteria();
// set population references:
Trainer trainer = trainerModuleScript.gameController.masterTrainer;
int curPlayer = trainer.CurPlayer;
playerRef = trainer.PlayerList[curPlayer-1]; // get current population instance
if(pendingCrossoverManager == null) {
pendingCrossoverManager = new CrossoverManager();
}
pendingCrossoverManager.CopyFromSourceCrossoverManager(playerRef.masterCupid);
// SET values from trainer data:
// MUTATION TAB!!!!
sliderMasterMutationRate.minValue = 0f; // set up slider bounds
sliderMasterMutationRate.maxValue = 1f;
sliderMaximumWeight.minValue = 0f; // set up slider bounds
sliderMaximumWeight.maxValue = 5f;
sliderMutationDriftScale.minValue = 0f; // set up slider bounds
sliderMutationDriftScale.maxValue = 1f;
sliderRemoveLinkChance.minValue = 0f; // set up slider bounds
sliderRemoveLinkChance.maxValue = 1f;
sliderAddLinkChance.minValue = 0f; // set up slider bounds
sliderAddLinkChance.maxValue = 1f;
sliderRemoveNodeChance.minValue = 0f; // set up slider bounds
sliderRemoveNodeChance.maxValue = 1f;
sliderAddNodeChance.minValue = 0f; // set up slider bounds
sliderAddNodeChance.maxValue = 1f;
sliderFunctionChance.minValue = 0f; // set up slider bounds
sliderFunctionChance.maxValue = 1f;
sliderLargeBrainPenalty.minValue = 0f; // set up slider bounds
sliderLargeBrainPenalty.maxValue = 0.5f;
sliderNewLinkMutateBonus.minValue = 1f;
sliderNewLinkMutateBonus.maxValue = 10f;
sliderNewLinkBonusDuration.minValue = 5;
sliderNewLinkBonusDuration.maxValue = 50;
sliderExistingNetworkBias.minValue = 0f;
sliderExistingNetworkBias.maxValue = 1f;
sliderExistingFromNodeBias.minValue = 0f;
sliderExistingFromNodeBias.maxValue = 1f;
// CROSSOVER TAB!!!
sliderCrossoverRandomLinkChance.minValue = 0f;
sliderCrossoverRandomLinkChance.maxValue = 1f;
// SPECIES TAB!!!!!
sliderSimilarityThreshold.minValue = 0f; // set up slider bounds
sliderSimilarityThreshold.maxValue = 10f;
sliderNeuronWeight.minValue = 0f;
sliderNeuronWeight.maxValue = 1f;
sliderLinkWeight.minValue = 0f;
sliderLinkWeight.maxValue = 1f;
sliderWeightWeight.minValue = 0f;
sliderWeightWeight.maxValue = 1f;
sliderNormalizeNeuron.minValue = 0f;
sliderNormalizeNeuron.maxValue = 1f;
sliderNormalizeLink.minValue = 0f;
sliderNormalizeLink.maxValue = 1f;
sliderNormalizeWeight.minValue = 0f;
sliderNormalizeWeight.maxValue = 1f;
sliderAdoptionRate.minValue = 0f; // set up slider bounds
sliderAdoptionRate.maxValue = 1f;
sliderSpeciesSizePenalty.minValue = 0f; // set up slider bounds
sliderSpeciesSizePenalty.maxValue = 0.5f;
sliderInterspeciesMatingRate.minValue = 0f; // set up slider bounds
sliderInterspeciesMatingRate.maxValue = 1f;
// BODY TAB!!!!
sliderMaxAttributeValueChange.minValue = 1f; // multiplier
sliderMaxAttributeValueChange.maxValue = 2f;
sliderNewSegmentChance.minValue = 0f;
sliderNewSegmentChance.maxValue = 0.05f;
sliderRemoveSegmentChance.minValue = 0f;
sliderRemoveSegmentChance.maxValue = 0.05f;
sliderSegmentProportionChance.minValue = 0f;
sliderSegmentProportionChance.maxValue = 0.2f;
sliderSegmentAttachSettingsChance.minValue = 0f;
sliderSegmentAttachSettingsChance.maxValue = 0.2f;
sliderJointSettingsChance.minValue = 0f;
sliderJointSettingsChance.maxValue = 0.2f;
sliderNewAddonChance.minValue = 0f;
sliderNewAddonChance.maxValue = 0.05f;
sliderRemoveAddonChance.minValue = 0f;
sliderRemoveAddonChance.maxValue = 0.05f;
sliderAddonSettingsChance.minValue = 0f;
sliderAddonSettingsChance.maxValue = 0.2f;
sliderRecursionChance.minValue = 0f;
sliderRecursionChance.maxValue = 0.05f;
sliderSymmetryChance.minValue = 0f;
sliderSymmetryChance.maxValue = 0.05f;
// COMMON!!!!
sliderSurvivalRate.minValue = 0f; // set up slider bounds
sliderSurvivalRate.maxValue = 1f;
sliderBreedingRate.minValue = 0f; // set up slider bounds
sliderBreedingRate.maxValue = 1f;
// Need to add the Toggles here ?!?????????????????????????????????????????
valuesChanged = false;
applyPressed = false;
UpdateUIWithCurrentData();
}
public static void Main(string[] args)
{
Random rand = new Random();
ItemSetController itemSetController = new ItemSetController(rand, ITERATION_COUNT);
PopulationSelector populationSelector = new PopulationSelector(1, 106, rand, ITERATION_COUNT);
CrossoverManager crossoverManager = new CrossoverManager(rand, ITERATION_COUNT);
SelectionManager selectionManager = new SelectionManager(rand);
MutationManager mutationManager = new MutationManager(rand, ITERATION_COUNT);
for (int i = 0; i < ITERATION_COUNT; i++)
{
itemSetController.addItem(new Item(rand.Next(1, 30), rand.Next(1, 30)), i);
}
itemSetController.ShowItems();
itemSetController.IniSets();
itemSetController.ShowSets();
string pairSelection;
do
{
Console.WriteLine("Выберите тип выборки пары: 1 - Лучшая пара 2 - Случайный");
pairSelection = Console.ReadLine();
} while (pairSelection != "1" && pairSelection != "2");
string crossover;
do
{
Console.WriteLine("Выберите вид кроссовера: 1 - Двухточечный(Случайный) 2 - Одноточечный классический");
crossover = Console.ReadLine();
} while (crossover != "1" && crossover != "2");
string mutation;
do
{
Console.WriteLine("Выберите вид мутации: 1 - изменеие бита на противоположный 2 - инверсия в случайном интервале");
mutation = Console.ReadLine();
} while (mutation != "1" && mutation != "2");
string selection;
do
{
Console.WriteLine("Выберите вид селекции: 1 - Бинарный турнир 2 - Пропорциональная");
selection = Console.ReadLine();
} while (selection != "1" && selection != "2");
do
{
if (pairSelection == "1")
{
populationSelector.ChooseBestPair(itemSetController.getSets());
}
else
{
populationSelector.ChoosePair(itemSetController.getSets());
}
Set[] childrens = new Set[CHILDS_COUNT];
int dividerPoint = rand.Next(1, ITERATION_COUNT - 1);
Set[] bestPair = populationSelector.getPair();
for (int i = 0; i < childrens.Length; i++)
{
childrens[i] = crossover == "1" ? crossoverManager.DoubleCross(bestPair, itemSetController.getItems()) :
crossoverManager.ClassicCross(bestPair, itemSetController.getItems(), dividerPoint);
if (mutation == "1")
{
mutationManager.RandomMutation(childrens[i]);
}
else
{
mutationManager.InversionMutation(childrens[i]);
}
}
populationSelector.NewPopulation(itemSetController.getSets(), selection == "1" ?
selectionManager.BinaryTournament(childrens, CHILDS_COUNT / 2) :
selectionManager.ProportionallySelection(childrens, CHILDS_COUNT / 2));
Console.WriteLine("Новая популяция:");
itemSetController.ShowSets();
populationSelector.ShowBestOf(itemSetController.getSets());
} while (!populationSelector.End(itemSetController.getSets(), 85) && populationSelector.getAge() != 10);
Console.ReadKey();
}
public void CopyFromSourceCrossoverManager(CrossoverManager sourceManager) {
tempName = sourceManager.tempName;
useMutation = sourceManager.useMutation;
useCrossover = sourceManager.useCrossover;
useSpeciation = sourceManager.useSpeciation;
//Mutation
masterMutationRate = sourceManager.masterMutationRate;
maximumWeightMagnitude = sourceManager.maximumWeightMagnitude;
mutationDriftScale = sourceManager.mutationDriftScale;
mutationRemoveLinkChance = sourceManager.mutationRemoveLinkChance;
mutationAddLinkChance = sourceManager.mutationAddLinkChance;
mutationRemoveNodeChance = sourceManager.mutationRemoveNodeChance;
mutationAddNodeChance = sourceManager.mutationAddNodeChance;
mutationActivationFunctionChance = sourceManager.mutationActivationFunctionChance;
largeBrainPenalty = sourceManager.largeBrainPenalty;
newLinkMutateBonus = sourceManager.newLinkMutateBonus;
newLinkBonusDuration = sourceManager.newLinkBonusDuration;
existingNetworkBias = sourceManager.existingNetworkBias;
existingFromNodeBias = sourceManager.existingFromNodeBias;
//Crossover
crossoverRandomLinkChance = sourceManager.crossoverRandomLinkChance;
//Species
speciesSimilarityThreshold = sourceManager.speciesSimilarityThreshold;
neuronWeight = sourceManager.neuronWeight;
linkWeight = sourceManager.linkWeight;
weightWeight = sourceManager.weightWeight;
normalizeExcess = sourceManager.normalizeExcess;
normalizeDisjoint = sourceManager.normalizeDisjoint;
normalizeLinkWeight = sourceManager.normalizeLinkWeight;
adoptionRate = sourceManager.adoptionRate;
largeSpeciesPenalty = sourceManager.largeSpeciesPenalty;
interspeciesBreedingRate = sourceManager.interspeciesBreedingRate;
// Body
maxAttributeValueChange = sourceManager.maxAttributeValueChange;
newSegmentChance = sourceManager.newSegmentChance;
removeSegmentChance = sourceManager.removeSegmentChance;
segmentProportionChance = sourceManager.segmentProportionChance;
segmentAttachSettingsChance = sourceManager.segmentAttachSettingsChance;
jointSettingsChance = sourceManager.jointSettingsChance;
newAddonChance = sourceManager.newAddonChance;
removeAddonChance = sourceManager.removeAddonChance;
addonSettingsChance = sourceManager.addonSettingsChance;
recursionChance = sourceManager.recursionChance;
symmetryChance = sourceManager.symmetryChance;
// Common
survivalRate = sourceManager.survivalRate;
survivalByRank = sourceManager.survivalByRank;
survivalStochastic = sourceManager.survivalStochastic;
survivalByRaffle = sourceManager.survivalByRaffle;
breedingRate = sourceManager.breedingRate;
breedingByRank = sourceManager.breedingByRank;
breedingStochastic = sourceManager.breedingStochastic;
breedingByRaffle = sourceManager.breedingByRaffle;
}
public void InitializePanelWithTrainerData()
{
DebugBot.DebugFunctionCall("TCrossoverUI; InitializePanelWithTrainerData(); ", debugFunctionCalls);
//CheckActivationCriteria();
// set population references:
Trainer trainer = trainerModuleScript.gameController.masterTrainer;
int curPlayer = trainer.CurPlayer;
playerRef = trainer.PlayerList[curPlayer-1]; // get current population instance
if(pendingCrossoverManager == null) {
pendingCrossoverManager = new CrossoverManager();
}
pendingCrossoverManager.CopyFromSourceCrossoverManager(playerRef.masterCupid);
// SET values from trainer data:
sliderMasterMutationRate.minValue = 0f; // set up slider bounds
sliderMasterMutationRate.maxValue = 0.2f;
//sliderMasterMutationRate.value = pendingCrossoverManager.masterMutationRate;
//textMasterMutationRate.text = pendingCrossoverManager.masterMutationRate.ToString();
sliderMaximumWeight.minValue = 0f; // set up slider bounds
sliderMaximumWeight.maxValue = 5f;
sliderMutationDriftScale.minValue = 0f; // set up slider bounds
sliderMutationDriftScale.maxValue = 1f;
sliderSeverLinkChance.minValue = 0f; // set up slider bounds
sliderSeverLinkChance.maxValue = 1f;
sliderAddLinkChance.minValue = 0f; // set up slider bounds
sliderAddLinkChance.maxValue = 1f;
sliderFunctionChance.minValue = 0f; // set up slider bounds
sliderFunctionChance.maxValue = 1f;
sliderNumSwapPositions.minValue = 1;
sliderNumSwapPositions.maxValue = 20;
sliderNumFactions.minValue = 1;
sliderNumFactions.maxValue = 20;
sliderMinNumParents.minValue = 1;
sliderMinNumParents.maxValue = 20;
sliderMaxNumParents.minValue = 1;
sliderMaxNumParents.maxValue = 20;
sliderSurvivalRate.minValue = 0f; // set up slider bounds
sliderSurvivalRate.maxValue = 1f;
sliderBreedingRate.minValue = 0f; // set up slider bounds
sliderBreedingRate.maxValue = 1f;
valuesChanged = false;
applyPressed = false;
UpdateUIWithCurrentData();
}
public void MakeRefToSourceCrossoverManager(CrossoverManager sourceManager)
{
//this = sourceManager;
}
public void CopyFromSourceCrossoverManager(CrossoverManager sourceManager)
{
tempName = sourceManager.tempName;
masterMutationRate = sourceManager.masterMutationRate;
maximumWeightMagnitude = sourceManager.maximumWeightMagnitude;
mutationDriftScale = sourceManager.mutationDriftScale;
mutationRemoveLinkChance = sourceManager.mutationRemoveLinkChance;
mutationAddLinkChance = sourceManager.mutationAddLinkChance;
mutationFunctionChance = sourceManager.mutationFunctionChance;
numSwapPositions = sourceManager.numSwapPositions;
numFactions = sourceManager.numFactions;
minNumParents = sourceManager.minNumParents;
maxNumParents = sourceManager.maxNumParents;
breedWithSimilar = sourceManager.breedWithSimilar;
survivalRate = sourceManager.survivalRate;
survivalByRank = sourceManager.survivalByRank;
survivalStochastic = sourceManager.survivalStochastic;
survivalByRaffle = sourceManager.survivalByRaffle;
breedingRate = sourceManager.breedingRate;
breedingByRank = sourceManager.breedingByRank;
breedingStochastic = sourceManager.breedingStochastic;
breedingByRaffle = sourceManager.breedingByRaffle;
}
public override object Read(ES2Reader reader)
{
CrossoverManager data = new CrossoverManager();
Read(reader, data);
return data;
}
public override void Read(ES2Reader reader, object c)
{
CrossoverManager data = (CrossoverManager)c;
// Add your reader.Read calls here to read the data into the object.
// Read the version number.
int fileVersion = reader.Read <int>();
// VERSION 0:
if (fileVersion >= 0)
{
data.adoptionRate = reader.Read <float>();
data.breedingByRaffle = reader.Read <bool>();
data.breedingByRank = reader.Read <bool>();
data.breedingRate = reader.Read <float>();
data.breedingStochastic = reader.Read <bool>();
data.crossoverRandomLinkChance = reader.Read <float>();
data.linkWeight = reader.Read <float>();
data.neuronWeight = reader.Read <float>();
data.existingFromNodeBias = reader.Read <float>();
data.existingNetworkBias = reader.Read <float>();
data.interspeciesBreedingRate = reader.Read <float>();
data.largeBrainPenalty = reader.Read <float>();
data.largeSpeciesPenalty = reader.Read <float>();
data.weightWeight = reader.Read <float>();
data.masterMutationRate = reader.Read <float>();
//data.maxBodyMutationFactor = reader.Read<float>();
data.maximumWeightMagnitude = reader.Read <float>();
data.mutationActivationFunctionChance = reader.Read <float>();
data.mutationAddLinkChance = reader.Read <float>();
data.mutationAddNodeChance = reader.Read <float>();
//data.mutationBodyChance = reader.Read<float>();
data.mutationDriftScale = reader.Read <float>();
data.mutationRemoveLinkChance = reader.Read <float>();
data.mutationRemoveNodeChance = reader.Read <float>();
data.newLinkBonusDuration = reader.Read <int>();
data.newLinkMutateBonus = reader.Read <float>();
data.normalizeDisjoint = reader.Read <float>();
data.normalizeExcess = reader.Read <float>();
data.normalizeLinkWeight = reader.Read <float>();
data.speciesSimilarityThreshold = reader.Read <float>();
// body:
data.maxAttributeValueChange = reader.Read <float>();
data.newSegmentChance = reader.Read <float>();
data.removeSegmentChance = reader.Read <float>();
data.segmentProportionChance = reader.Read <float>();
data.segmentAttachSettingsChance = reader.Read <float>();
data.jointSettingsChance = reader.Read <float>();
data.newAddonChance = reader.Read <float>();
data.removeAddonChance = reader.Read <float>();
data.addonSettingsChance = reader.Read <float>();
data.recursionChance = reader.Read <float>();
data.symmetryChance = reader.Read <float>();
data.survivalByRaffle = reader.Read <bool>();
data.survivalByRank = reader.Read <bool>();
data.survivalRate = reader.Read <float>();
data.survivalStochastic = reader.Read <bool>();
data.useCrossover = reader.Read <bool>();
data.useMutation = reader.Read <bool>();
data.useSpeciation = reader.Read <bool>();
data.savedNextNodeInnov = reader.Read <int>();
data.savedNextAddonInnov = reader.Read <int>();
if (fileVersion >= 1)
{
// new attributes
}
}
}
public override void Write(object obj, ES2Writer writer)
{
CrossoverManager data = (CrossoverManager)obj;
// Add your writer.Write calls here.
writer.Write(0); // Version 1 is current version number
// Make sure to edit Read() function to properly handle version control!
// VERSION 0:
writer.Write(data.adoptionRate);
writer.Write(data.breedingByRaffle);
writer.Write(data.breedingByRank);
writer.Write(data.breedingRate);
writer.Write(data.breedingStochastic);
writer.Write(data.crossoverRandomLinkChance);
writer.Write(data.linkWeight);
writer.Write(data.neuronWeight);
writer.Write(data.existingFromNodeBias);
writer.Write(data.existingNetworkBias);
writer.Write(data.interspeciesBreedingRate);
writer.Write(data.largeBrainPenalty);
writer.Write(data.largeSpeciesPenalty);
writer.Write(data.weightWeight);
writer.Write(data.masterMutationRate);
//writer.Write(data.maxBodyMutationFactor);
writer.Write(data.maximumWeightMagnitude);
writer.Write(data.mutationActivationFunctionChance);
writer.Write(data.mutationAddLinkChance);
writer.Write(data.mutationAddNodeChance);
//writer.Write(data.mutationBodyChance);
writer.Write(data.mutationDriftScale);
writer.Write(data.mutationRemoveLinkChance);
writer.Write(data.mutationRemoveNodeChance);
writer.Write(data.newLinkBonusDuration);
writer.Write(data.newLinkMutateBonus);
writer.Write(data.normalizeDisjoint);
writer.Write(data.normalizeExcess);
writer.Write(data.normalizeLinkWeight);
writer.Write(data.speciesSimilarityThreshold);
//Body:
writer.Write(data.maxAttributeValueChange);
writer.Write(data.newSegmentChance);
writer.Write(data.removeSegmentChance);
writer.Write(data.segmentProportionChance);
writer.Write(data.segmentAttachSettingsChance);
writer.Write(data.jointSettingsChance);
writer.Write(data.newAddonChance);
writer.Write(data.removeAddonChance);
writer.Write(data.addonSettingsChance);
writer.Write(data.recursionChance);
writer.Write(data.symmetryChance);
writer.Write(data.survivalByRaffle);
writer.Write(data.survivalByRank);
writer.Write(data.survivalRate);
writer.Write(data.survivalStochastic);
writer.Write(data.useCrossover);
writer.Write(data.useMutation);
writer.Write(data.useSpeciation);
writer.Write(data.savedNextNodeInnov);
writer.Write(data.savedNextAddonInnov);
}
