private void GenerateInitialNetsAsCopy(NEATNet net)
{
generationNumber = 0; //0 since simulaiton has no been started yet
testCounter = 0; //none have been tested
speciesManager = new Species(this);
for (int i = 0; i < populationSize; i++)
{ //run through population size
NEATNet netCopy = new NEATNet(net); //create net with consultor, perceptron information and test time
netCopy.SetNetFitness(0f);
netCopy.SetTimeLived(0f);
netCopy.SetTestTime(testTime);
netCopy.ClearNodeValues();
netCopy.GenerateNeuralNetworkFromGenome(); //< NEW LSES ADDITION
Population population = speciesManager.ClosestSpecies(netCopy);
if (population == null)
{
population = speciesManager.CreateNewSpecie(new Color(UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f)));
population.Add(netCopy);
}
else
{
population.Add(netCopy);
}
}
}
private static void initializeGeneticAlgorithm(
int populationSize,
int parentalChromosomesSurviveCount,
OptimizableNeuralNetwork baseNeuralNetwork)
{
Population <OptimizableNeuralNetwork> brains = new Population <OptimizableNeuralNetwork>();
for (int i = 0; i < (populationSize - 1); i++)
{
if (baseNeuralNetwork == null)
{
brains.Add(NeuralNetworkDrivenAgent.randomNeuralNetworkBrain());
}
else
{
brains.Add(baseNeuralNetwork.Mutate());
}
}
if (baseNeuralNetwork != null)
{
brains.Add(baseNeuralNetwork);
}
else
{
brains.Add(NeuralNetworkDrivenAgent.randomNeuralNetworkBrain());
}
ga = new GeneticAlgorithm <OptimizableNeuralNetwork, double>(brains, TournamentEnvironmentFitness.Calculate);
ga.ParentChromosomesSurviveCount = parentalChromosomesSurviveCount;
}
public void PopuationEnumerationShouldNotProduceNull()
{
void AssertNoNulls()
{
foreach (var p in population)
{
p.Should().NotBeNull();
}
}
AssertNoNulls();
population.Add(CreatePhenotypeMock(1).Object);
AssertNoNulls();
population.Fill(() => CreatePhenotypeMock(1).Object);
AssertNoNulls();
population.Clear(2, EAMode.MaximizeFitness);
AssertNoNulls();
population.Fill(() => CreatePhenotypeMock(1).Object);
population.Clear(1, EAMode.MaximizeFitness);
AssertNoNulls();
population.Clear();
AssertNoNulls();
}
public void TestSelection()
{
var offspring = new Population(2);
var population = new Population(2);
var o0 = new P("o0", 1.0);
offspring.Add(o0);
var o1 = new P("o1", 0.0);
offspring.Add(o1);
var p0 = new P("p0", 1.0);
population.Add(p0);
var p1 = new P("p1", 0.0);
population.Add(p1);
var genmix = new GenerationalReplacementAdultSelection();
genmix.SelectAdults(offspring, population, 2, EAMode.MaximizeFitness);
Assert.IsTrue(population.Contains(o0));
Assert.IsTrue(population.Contains(o1));
Assert.IsFalse(population.Contains(p0));
Assert.IsFalse(population.Contains(p1));
}
public void ClearWithOneEliteShouldLeavePhenotypeWithMinFitness()
{
var popsize = 3;
population = new Population(popsize);
var m0 = new Mock <IPhenotype>();
m0.SetupGet(p => p.Fitness).Returns(1.0);
population.Add(m0.Object);
var m1 = new Mock <IPhenotype>();
m1.Setup(p => p.Fitness).Returns(0.8);
population.Add(m1.Object);
var m2 = new Mock <IPhenotype>();
m2.Setup(p => p.Fitness).Returns(0.6);
population.Add(m2.Object);
population.Evaluate(true, null);
population.Clear(1, EAMode.MinimizeFitness);
for (int i = 1; i < population.Size; i++)
{
Assert.IsNull(population[i]);
}
population[0].Fitness.Should().Be(0.6);
}
public void TestSelectionWithMinimizing()
{
var offspring = new Population(2);
var population = new Population(2);
var o0 = new P("o0", 1.0);
offspring.Add(o0);
var o1 = new P("o1", 0.0);
offspring.Add(o1);
var p0 = new P("p0", 1.0);
population.Add(p0);
var p1 = new P("p1", 0.0);
population.Add(p1);
var genmix = new GenerationalMixingAdultSelection(new DefaultRandomNumberGenerator());
genmix.SelectAdults(offspring, population, 2, EAMode.MinimizeFitness);
Assert.IsFalse(population.Contains(o0));
Assert.IsTrue(population.Contains(o1));
Assert.IsFalse(population.Contains(p0));
Assert.IsTrue(population.Contains(p1));
}
private void CreateChildrenAndPutThemIntoPopulation(Population population)
{
for (var k = 0; k < population.Count / 2; k++)
{
population.Add(CreateChildrenFromParents(population[k], population[k + 1]));
population.Add(CreateChildrenFromParents(population[k + 1], population[k]));
}
}
/// <summary>
/// Generate initial neural network, where all inputs perceptrons are connected to all output perceptrons.
/// Each of these nerual networks is mutated one time for slight change.
/// And then they are paired up into species.
/// </summary>
private void GenerateInitialNets()
{
generationNumber = 0; //0 since simulaiton has no been started yet
testCounter = 0; //none have been tested
speciesManager = new Species(this);
//species = new List<List<NEATNet>>(); //create an empty population list
for (int i = 0; i < populationSize; i++)
{ //run through population size
NEATNet net = new NEATNet(consultor, numberOfInputPerceptrons, numberOfOutputPerceptrons, testTime); //create net with consultor, perceptron information and test time
net.Mutate(); //mutate once for diversity (must make sure SJW's aren't triggered)
net.GenerateNeuralNetworkFromGenome(); //< NEW LSES ADDITION
Population population = speciesManager.ClosestSpecies(net);
if (population == null)
{
population = speciesManager.CreateNewSpecie(new Color(UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f)));
population.Add(net);
}
else
{
population.Add(net);
}
/*if (species.Count == 0) { //if nothing exists yet
* List<NEATNet> newSpecies = new List<NEATNet>(); //create a new species
* newSpecies.Add(net); //add net to this new species
* species.Add(newSpecies); //add new species to species list
* }
* else { //if at least one species exists
* int numberOfSpecies = species.Count; //save species count
* int location = -1; //-1 means no species match found, 0 >= will mean a species match has been found at a given index
*
* for (int j = 0; j < numberOfSpecies; j++) { //run through species count
* int numberOfNets = species[j].Count; //number of organisum in species at index j
* int randomIndex = UnityEngine.Random.Range(0, numberOfNets); //pick a random network within this species
*
* if (NEATNet.SameSpeciesV2(species[j][randomIndex], net)) { //check if new network and random network belong to the same species
* location = j; //new species can be added to index j
* break; //break out of loop
* }
* }
*
* if (location == -1) { //if no species found
* //create new species and add to that
* List<NEATNet> newSpecies = new List<NEATNet>();
* newSpecies.Add(net);
* species.Add(newSpecies);
* }
* else { //found species that match this network
* species[location].Add(net); //add net to the matched species list
* }
* }*/
}
}
void PopulateNextGeneration(Population <T> nextGen, List <Individual <T> > fittestList)
{
fittestList.ForEach(f => nextGen.Add(f));
while (nextGen.PopulationSize < _population.PopulationSize)
{
Individual <T>[] parents = SelectParents(fittestList);
Individual <T> child = CrossoverProvider.GetNextCrossover().PerformCrossover(parents [0], parents [1]);
nextGen.Add(child);
}
;
}
public void CrossoverPopulation(Population population)
{
for (int i = 0; i < population.RemoveCount / 2; i++)
{
var firstParent = population.GetRandomMember();
var secondParent = population.GetRandomMember();
var children = Crossover(firstParent, secondParent, population.DietaryReferenceIntakes, population.Foods);
population.Add(children[0]);
population.Add(children[1]);
}
}
public void SelectAdults(Population offspring, Population population, int n, EAMode mode)
{
var roulette = new Roulette <IPhenotype>(_rng, offspring.Size + population.Size);
if (mode == EAMode.MaximizeFitness)
{
roulette.AddAll(offspring, e => e.Fitness);
roulette.AddAll(population, e => e.Fitness);
population.Clear();
for (int i = 0; i < n; i++)
{
population.Add(roulette.SpinAndRemove());
}
}
else if (mode == EAMode.MinimizeFitness)
{
double p_max = Double.MinValue;
double p_min = Double.MaxValue;
foreach (var e in offspring)
{
p_max = Math.Max(e.Fitness, p_max);
p_min = Math.Min(e.Fitness, p_min);
}
foreach (var e in population)
{
p_max = Math.Max(e.Fitness, p_max);
p_min = Math.Min(e.Fitness, p_min);
}
foreach (var e in offspring)
{
roulette.Add(e, (p_max + p_min) - e.Fitness);
}
foreach (var e in population)
{
roulette.Add(e, (p_max + p_min) - e.Fitness);
}
population.Clear();
for (int i = 0; i < n; i++)
{
population.Add(roulette.SpinAndRemove());
}
}
else
{
throw new NotImplementedException(mode.ToString());
}
}
/// <summary>
/// Operator's operate method.
/// </summary>
/// <param name="parents">parents</param>
/// <param name="offspring">offspring</param>
public void Operate(Population parents, Population offspring)
{
int size = parents.GetPopulationSize();
for (int i = 0; i < size / 2; i++)
{
Individual p1 = parents.Get(2 * i);
Individual p2 = parents.Get(2 * i + 1);
Individual o1 = (Individual)p1.Clone();
Individual o2 = (Individual)p2.Clone();
if (rng.NextDouble() < xOverProb)
{
HashSet <int> indices = new HashSet <int>();
bool flipper = true;
// select crossover points
while (indices.Count < numberOfPoints)
{
indices.Add(rng.NextInt(p1.Length()));
}
// perform the crossover
for (int j = 0; j < p1.Length(); j++)
{
if (indices.Contains(j))
{
flipper = flipper == true ? false : true;
}
if (flipper)
{
o1.SetActivityOnEdge(j, p1.IsActiveOnEdge(j));
o2.SetActivityOnEdge(j, p2.IsActiveOnEdge(j));
}
else
{
o1.SetActivityOnEdge(j, p2.IsActiveOnEdge(j));
o2.SetActivityOnEdge(j, p1.IsActiveOnEdge(j));
}
}
o1.changed = true;
o2.changed = true;
}
offspring.Add(o1);
offspring.Add(o2);
}
}
public bool LoadGeneration(string filePath, bool Encript)
{
if (!System.IO.File.Exists(filePath))
{
return(false);
}
SaveData.GeneticSaveData <T> save = null;
if (Encript)
{
save = Utils.FileReadWrite.ReadEncryptedFromBinaryFile <SaveData.GeneticSaveData <T> >(filePath);
}
else
{
save = Utils.FileReadWrite.ReadFromBinaryFile <SaveData.GeneticSaveData <T> >(filePath);
}
Generation = save.Generation;
for (int i = 0; i < save.PopulationGenes.Count; i++)
{
if (i >= Population.Count)
{
Population.Add(new DNA <T>(dnaSize, random, getRandomGene, fitnessFunction, false));
}
Array.Copy(save.PopulationGenes[i], Population[i].Genes, dnaSize);
}
return(true);
}
/// <summary>
/// Selecting howMany individuals from from to to
/// </summary>
/// <param name="howMany">number of individuals to select</param>
/// <param name="from">old population</param>
/// <param name="to">new population</param>
public void Select(int howMany, Population from, Population to)
{
double fitnessSum = 0.0;
for (int i = 0; i < from.GetPopulationSize(); i++)
{
fitnessSum += from.Get(i).GetFitnessValue();
}
double[] fitnesses = new double[from.GetPopulationSize()];
for (int i = 0; i < fitnesses.Length; i++)
{
fitnesses[i] = from.Get(i).GetFitnessValue() / fitnessSum;
}
for (int i = 0; i < howMany; i++)
{
double ball = rng.NextDouble();
double sum = 0;
for (int j = 0; j < fitnesses.Length; j++)
{
sum += fitnesses[j];
if (sum > ball)
{
to.Add((Individual)from.Get(j).Clone());
break;
}
}
}
}
public ViewModel()
{
this.Population = new ObservableCollection <Model>();
ChartThemes = Enum.GetValues(typeof(Syncfusion.SfSkinManager.VisualStyles)).OfType <object>().ToList();
ChartThemes.RemoveAt(0);
DateTime date = new DateTime(2006, 1, 1);
Population.Add(new Model {
Year = date.AddYears(1), China = 1307.56, India = 1101.32
});
Population.Add(new Model {
Year = date.AddYears(2), China = 1314.48, India = 1117.73
});
Population.Add(new Model {
Year = date.AddYears(3), China = 1321.29, India = 1134.02
});
Population.Add(new Model {
Year = date.AddYears(4), China = 1328.2, India = 1150.2
});
Population.Add(new Model {
Year = date.AddYears(5), China = 1334.5, India = 1166.23
});
Population.Add(new Model {
Year = date.AddYears(6), China = 1340.91, India = 1186
});
Population.Add(new Model {
Year = date.AddYears(7), China = 1347.35, India = 1210.57
});
Population.Add(new Model {
Year = date.AddYears(8), China = 1353.04, India = 1213.37
});
}
public void TestGetProbabilitySelectorWithMinimizeMode()
{
var pMocks = new List <Mock <IPhenotype> >();
pMocks.Add(CreatePhenotypeMock(1));
pMocks.Add(CreatePhenotypeMock(2));
pMocks.Add(CreatePhenotypeMock(3));
pMocks.Add(CreatePhenotypeMock(5));
var expected = new List <double>()
{
5, 4, 3, 1
};
population = new Population(4);
foreach (var mock in pMocks)
{
population.Add(mock.Object);
}
var probSelector = population.GetProbabilitySelector(EAMode.MinimizeFitness);
for (int i = 0; i < pMocks.Count; i++)
{
probSelector(pMocks[i].Object).Should().Be(expected[i]);
}
}
/// <summary>
/// Selecting howMany individuals from from to to
/// </summary>
/// <param name="howMany">number of individuals to select</param>
/// <param name="from">old population</param>
/// <param name="to">new population</param>
public void Select(int howMany, Population from, Population to)
{
for (int i = 0; i < howMany; i++)
{
List <int> players = new List <int>();
for (int j = 0; j < competitors; j++)
{
players.Add(rng.NextInt(from.GetPopulationSize()));
}
while (players.Count > 1)
{
if (from.Get(players[0]).GetFitnessValue() > from.Get(players[1]).GetFitnessValue() &&
rng.NextDouble() > weakerProb)
{
players.Remove(players[1]);
}
else
{
players.Remove(players[0]);
}
}
to.Add((Individual)from.Get(players[0]).Clone());
}
}
internal static Population <List <Genome>, Problem, Fitness> InitializePopulation(
Problem problem, Second target, int population_size, int round_count)
{
IRandomGenerator random = new RandomGenerator();
// generate a list of cities to place.
List <int> cities = new List <int>();
for (int city_to_place = 0; city_to_place < problem.Cities; city_to_place++)
{
cities.Add(city_to_place);
}
// create the population
Population <List <Genome>, Problem, Fitness> population = new Population <List <Genome>, Problem, Fitness>(
null, false);
// create the fitness calculator.
FitnessCalculator fitness_calculator = new FitnessCalculator(5);
while (population.Count < population_size)
{
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.VRP.MultiSalesman.Facade", System.Diagnostics.TraceEventType.Information,
"Initializing population individual {0}/{1}...", population.Count + 1, population_size);
// create copy of cities
List <int> cities_list = new List <int>(cities);
// create new individuals.
Individual individual =
new Individual(new List <Genome>());
// place one random city in each round.
for (int round_idx = 0; round_idx < round_count; round_idx++)
{
// select a random city to place.
int city_idx = random.Generate(cities_list.Count);
int city = cities_list[city_idx];
cities_list.RemoveAt(city_idx);
// create new genome.
Genome genome = new Genome();
genome.Add(city);
individual.Genomes.Add(genome);
}
individual = BestPlacementHelper.Do(
problem,
fitness_calculator,
individual,
cities_list);
// add inidividual to the population.
population.Add(individual);
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.VRP.MultiSalesman.Facade", System.Diagnostics.TraceEventType.Information,
"Done!");
}
return(population);
}
// init demographics
void initProfession(int professionID, int Ammount)
{
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
// Type type = getProfession(professionID);
Profession prof = (Profession)professionID;
for (int j = 0; j < Ammount; j++)
{
// try {
// Person temp = (Person)Activator.CreateInstance(type);
// Population.Add(temp);
// Demographics[professionID].Add(temp);
// } catch (Exception e) {
// Console.WriteLine("Error: Could not initialize Person from type");
// Console.WriteLine(e);
// }
Person temp = prof.ToPerson();
Population.Add(temp);
Demographics[professionID].Add(temp);
}
// test
Console.WriteLine(prof.ToString() + "\t" + stopwatch.Elapsed.TotalMilliseconds.ToString());
}
private static void InitialisePopulation()
{
for (var i = 0; i < PopulationSize; i++)
{
Population.Add(new Chromosome(Random));
}
}
/// <summary>
/// Operator operate method
/// </summary>
/// <param name="parents">parents</param>
/// <param name="offspring">offspring</param>
public void Operate(Population parents, Population offspring)
{
int size = parents.GetPopulationSize();
for (int i = 0; i < size; i++)
{
Individual p1 = parents.Get(i);
Individual o1 = (Individual)p1.Clone();
if (rng.NextDouble() < mutationProbability)
{
// any nondetoured edge might get activated
List <Edge> nondetouredEdges = p1.GetUndetoured();
if (nondetouredEdges.Count > 0)
{
foreach (var edge in Program.graph.GetEdges())
{
if (rng.NextDouble() < bitFlipProbability &&
nondetouredEdges.Contains(edge))
{
o1.SetActivityOnEdge(edge.ID, 1);
o1.changed = true;
}
}
}
}
offspring.Add(o1);
}
}
/// <summary>
/// Initializes the populations
/// </summary>
/// <param name="initialPopulation">Size of the initial population</param>
private void Initialize(int initialPopulation)
{
for (var i = 0; i < initialPopulation; i++)
{
var enumeration = Enumerable.Range(0, Configuration.People.Count).Shuffle(Random).ToList();
var arrangement = new Arrangement(Configuration.NumberOfTables, Configuration.PeoplePerTable);
foreach (var index in enumeration)
{
while (true)
{
var nextTable = Random.Next(0, Configuration.NumberOfTables);
var nextSeat = Random.Next(0, Configuration.PeoplePerTable);
if (arrangement.Tables[nextTable].People[nextSeat] == null)
{
arrangement.Tables[nextTable].People[nextSeat] = Configuration.People[index];
break;
}
}
}
arrangement.Score = CalculateFitness(arrangement);
Population.Add(arrangement);
}
}
void RespawnBankruptPeople()
{
// divide number of bankrupt people in profAmmount ammount of integer parts
int [] afterDiv = Helper.IntegerDivision(DeadPeople.Count, profAmmount);
for (int i = 0; i < DeadPeople.Count; i++)
{
// delete person references
Person person = DeadPeople[i];
Population.Remove(person);
Demographics[(int)person.Role].Remove(person);
// lostCash += person.Cash;
currentMoneySupply -= person.Cash;
}
for (int prof = 0; prof < afterDiv.Length; prof++)
{
for (int i = 0; i < afterDiv[prof]; i++)
{
// check money supply
decimal cash = 0;
if (MoneySupply - currentMoneySupply > 10)
{
currentMoneySupply += 10;
cash = 10;
}
// initialize new person
// Person temp = (Person)Activator.CreateInstance(type);
Person temp = ((Profession)prof).ToPerson();
temp.Transaction(cash, 0, 0);
// create references
Demographics[prof].Add(temp);
Population.Add(temp);
}
}
}
public void SelectAdults(Population offspring, Population population, int n, EAMode eamode)
{
population.Clear();
for (int i = 0; i < n; i++)
{
population.Add(offspring[i]);
}
}
private void ConvertBoidPopulationToViewModels(object sender, System.Collections.Specialized.NotifyCollectionChangedEventArgs e)
{
Population.Clear();
foreach (Boid boid in world.Population)
{
Population.Add(new BoidViewModel(boid));
}
}
private void HandleChange(object sender, NotifyCollectionChangedEventArgs e)
{
foreach (var item in e.NewItems)
{
var boid = (Boid)item;
var boidVM = new BoidViewModel(boid);
Population.Add(boidVM);
}
}
private void InitializePopulation()
{
for (var i = 0; i < PopulationSize; i++)
{
var chromosome = new Chromosome(ChromosomeSize);
chromosome.Fitness = FitnessFunction.CalculateFitness(chromosome);
Population.Add(chromosome);
}
}
private void InitializePopulation()
{
for (int i = 0; i < PopulationSize; i++)
{
var chromosome = new Chromosome(BusStops, Students);
chromosome.Fitness = FitnessFunction.CalculateFitness(chromosome);
Population.Add(chromosome);
}
}
public void LoadData(string filePath)
{
SaveData save = FileReadWrite.ReadFromJsonFile <SaveData>(filePath);
for (int i = 0; i < loadExternalGenes; i++)
{
Population.Add(save.SavedMember[i]);
}
}
private void generateNeuroPopulation_()
{
var networkState = NeuralNetwork.GetNetworkState();
for (var i = 0; i < TestPopulationCount; i++)
{
Population.Add(generateNeuroChromosomeByTemplate(networkState));
}
}
public bool testCrossoverPopulation()
{
Population parents = new Population();
Chromosome[] chr_tab = { new Chromosome(new bool[] {false, false, false}), new Chromosome(new bool[] { true, true, true })};
Individual[] ind_tab = new Individual[4];
for (int i = 0; i < 4; i++)
{
ind_tab[i] = new Individual(chr_tab[i % 2]);
parents.Add(ind_tab[i]);
}
int[] permutation = new int[] { 0, 2, 1, 3};
int random1 = 2;
int random2 = 0;
Population orphans = this.crossoverHelper(parents, random1, random2, permutation);
for (int i = 0; i < 4; i++)
{
if (i < 2)
{
for (int j = 0; j < orphans.getIndividual(0).chromosome.Count; j++)
{
if (orphans.getIndividual(i).chromosome[j] != false) return false;
}
}
else
{
for (int j = 0; j < orphans.getIndividual(0).chromosome.Count; j++)
{
if (orphans.getIndividual(i).chromosome[j] != true) return false;
}
}
}
return true;
}
/// <summary>
/// Solve the next iteration of the algorithm.
/// </summary>
public void SolveStep()
{
// checkPopDupes();
if (this.properties.Objectives.Length < 1)
{
throw new Exception("You cannot optimise with 0 objectives.");
}
this.iteration = this.Iteration + 1;
Console.WriteLine("Solving step {0}...", this.Iteration);
this.progress = 0;
var sw = Stopwatch.StartNew();
// var r = new Population(population[0].Union(population[1]));
/*
foreach (var c in r)
{
double totalDistance = 0.0;
foreach (var c2 in r)
{
/*
totalDistance += Math.Max(Math.Max(Math.Abs(c.Fitness.PercentBuses - c2.Fitness.PercentBuses) ,
Math.Abs(c.Fitness.PercentTrains - c2.Fitness.PercentTrains)) ,
Math.Abs(c.Fitness.PercentTrams - c2.Fitness.PercentTrams));
totalDistance += Math.Sqrt(Math.Pow(c.Fitness.PercentBuses - c2.Fitness.PercentBuses,2.0)+
Math.Pow(c.Fitness.PercentTrains - c2.Fitness.PercentTrains,2.0) +
Math.Pow(c.Fitness.PercentTrams - c2.Fitness.PercentTrams,2.0));
}
c.Fitness.DiversityMetric = totalDistance;
}
double jtMaxTime = r.Max(c => c.Fitness.TotalJourneyTime).TotalHours;
double jtMinTime = r.Min(c => c.Fitness.TotalJourneyTime).TotalHours;
double ttMaxTime = r.Max(c => c.Fitness.TotalDistance);
double ttMinTime = r.Min(c => c.Fitness.TotalDistance);
double minDM = r.Min(c => c.Fitness.DiversityMetric);
double maxDM = r.Max(c => c.Fitness.DiversityMetric);
int maxChanges = r.Max(c => c.Fitness.Changes);
int minChanges = r.Min(c => c.Fitness.Changes);
foreach (var c in r)
{
c.Fitness.NormalisedJourneyTime = (c.Fitness.TotalJourneyTime.TotalHours-jtMinTime) / (jtMaxTime - jtMinTime);
c.Fitness.NormalisedChanges = (double)(c.Fitness.Changes-minChanges) / (maxChanges - minChanges);
c.Fitness.NormalisedTravelTime = (c.Fitness.TotalDistance-ttMinTime) / (ttMaxTime - ttMinTime);
c.Fitness.DiversityMetric = (c.Fitness.DiversityMetric-minDM)/(maxDM - minDM);
if (double.IsInfinity(c.Fitness.NormalisedChanges))
{
c.Fitness.NormalisedChanges = 0;
}
if ((double.IsInfinity(c.Fitness.NormalisedJourneyTime)))
{
c.Fitness.NormalisedJourneyTime = 0;
}
if ((double.IsInfinity(c.Fitness.NormalisedTravelTime)))
{
c.Fitness.NormalisedTravelTime = 0;
}
if ((double.IsInfinity(c.Fitness.DiversityMetric)))
{
c.Fitness.DiversityMetric = 0;
}
Console.WriteLine("{0}, {1},{2},{3}", c.Fitness.NormalisedJourneyTime, c.Fitness.NormalisedChanges, c.Fitness.PercentBuses, c.Fitness.PercentTrains);
c.N = 0;
c.Rank = 0;
//c.Distance = 0;
}
this.nonDominatedSort(r);
var ranks = r.GroupBy(g => g.Rank);
foreach (var rank in ranks)
{
foreach (var c1 in rank)
{
foreach (var c2 in rank)
{
Assert.That(!this.Dominates(c1, c2), "Members of a front should be non dominate to each other.");
}
}
}
*/
this.population[1] = new Population();
for (int i = 0; i < this.Properties.PopulationSize / 2; i++)
{
var first = this.TournamentSelect();
var second = this.TournamentSelect();
Assert.That(first.DepartureTime != default(DateTime) && second.DepartureTime != default(DateTime));
bool doCrossover = this.random.NextDouble() <= this.Properties.CrossoverRate;
bool doMutation = this.random.NextDouble() <= this.Properties.MutationRate;
Critter[] children = doCrossover
? this.Properties.Breeder.Crossover(first, second)
: new[] { first, second };
if (doMutation)
{
children[0] = this.Properties.Mutator.Mutate(children[0]);
children[1] = this.Properties.Mutator.Mutate(children[1]);
}
Assert.That(
children[0].DepartureTime != default(DateTime) && children[1].DepartureTime != default(DateTime));
if (doCrossover || doMutation)
{
children[0].Fitness = this.Properties.FitnessFunction.GetFitness(
children[0].Route, children[0].DepartureTime);
children[1].Fitness = this.Properties.FitnessFunction.GetFitness(
children[1].Route, children[1].DepartureTime);
}
// var ff = (AlFitnessFunction)this.properties.FitnessFunction;
Assert.That(
children[0].DepartureTime != default(DateTime) && children[1].DepartureTime != default(DateTime));
this.population[1].AddRange(children);
this.progress++;
}
/*
var q = new List<Critter>(this.Properties.PopulationSize);
foreach (var c1 in f)
{
c1.Clear();
}
f.Clear();
for (int i = 0; i < this.Properties.PopulationSize/2; i ++)
{
var first = TournamentSelect();
var second = TournamentSelect();
Assert.That(first.departureTime != default(DateTime) && second.departureTime != default(DateTime));
bool doCrossover = this.random.NextDouble() <= this.Properties.CrossoverRate;
bool doMutation = this.random.NextDouble() <= this.Properties.MutationRate;
Critter[] children = doCrossover
? this.Properties.Breeder.Crossover(first, second)
: new[] { first, second };
if (doMutation)
{
children[0] = this.Properties.Mutator.Mutate(children[0]);
children[1] = this.Properties.Mutator.Mutate(children[1]);
}
Assert.That(children[0].departureTime != default(DateTime) && children[1].departureTime != default(DateTime));
if (doCrossover || doMutation)
{
children[0].Fitness = this.Properties.FitnessFunction.GetFitness(children[0].Route, children[0].departureTime);
children[1].Fitness = this.Properties.FitnessFunction.GetFitness(children[1].Route, children[1].departureTime);
}
//var ff = (AlFitnessFunction)this.properties.FitnessFunction;
Assert.That(children[0].departureTime != default(DateTime) && children[1].departureTime != default(DateTime));
q.AddRange(children);
progress++;
}
var r = this.population.Select(p => (Critter)p.Clone()).ToList();
checkPopDupes();
//r.AddRange(this.population);
r.AddRange(q);
double maxTime = r.Max(c => c.Fitness.TotalJourneyTime).TotalHours;
double minTime = r.Min(c => c.Fitness.TotalJourneyTime).TotalHours;
int maxChanges = r.Max(c => c.Fitness.Changes);
int minChanges = r.Min(c => c.Fitness.Changes);
foreach (var c in r)
{
c.Fitness.NormalisedJourneyTime = c.Fitness.TotalJourneyTime.TotalHours / (maxTime - minTime);
c.Fitness.NormalisedChanges = (double) c.Fitness.Changes / (maxChanges - minChanges);
Console.WriteLine("{0}, {1},{2},{3}",c.Fitness.NormalisedJourneyTime,c.Fitness.NormalisedChanges,c.Fitness.PercentBuses,c.Fitness.PercentTrains);
c.N = 0;
c.Rank = 0;
c.Distance = 0;
}
//this.crowdingDistanceAssignment(r);
this.nonDominatedSort(r);
foreach (var front in Fronts)
{
foreach (var c1 in front)
{
foreach (var c2 in front)
{
Assert.That(!this.Dominates(c1,c2), "Members of a front should be non dominate to each other.");
}
}
}
checkPopDupes();
this.population.Clear();
int j = 0;
while (this.population.Count + this.Fronts[j].Count <= this.Properties.PopulationSize)
{
var front = this.Fronts[j];
this.crowdingDistanceAssignment(front);
this.population.AddRange(front);
j++;
}
for (int i = 0; i < this.Fronts.Count; i++)
{
var front = this.Fronts[i];
foreach (var critter in front)
{
Assert.That(critter.Rank == i + 1);
}
}
/*
for (int i = 0; i < this.Fronts.Count; i++)
{
var front = this.Fronts[i];
foreach (var critter in front)
{
critter.Rank = i + 1;
}
}
var ranks = this.population.GroupBy(g => g.Rank);
foreach (var rank in ranks)
{
foreach (var c1 in rank)
{
foreach (var c2 in rank)
{
Assert.That(!this.Dominates(c1, c2), "Members of a front should be non dominate to each other.");
}
}
}
checkPopDupes();
this.Fronts[j].Sort(CompareCritters);
/*
this.Fronts[j].Sort((c1, c2) =>
{
if (c1.Rank < c2.Rank || (c1.Rank == c2.Rank && c1.Distance > c2.Distance))
{
return 1;
}
return -1;
});
this.population.AddRange(this.Fronts[j].GetRange(0, this.Properties.PopulationSize-this.population.Count));
checkPopDupes();
/*
foreach (var eliteCritter in eliteCritters)
{
Tools.ToLinkedNodes(eliteCritter.Route.GetNodes(true));
}
/*
this.nonDominatedSort(this.population);
var ranks = this.population.GroupBy(g => g.Rank);
foreach (var rank in ranks)
{
foreach (var c1 in rank)
{
foreach (var c2 in rank)
{
Assert.That(!this.Dominates(c1, c2), "Members of a front should be non dominate to each other.");
}
}
}
sw.Stop();
this.result.Totaltime = sw.Elapsed;
ranks = this.population.GroupBy(g => g.Rank);
foreach (var rank in ranks)
{
foreach (var c1 in rank)
{
foreach (var c2 in rank)
{
Assert.That(!this.Dominates(c1, c2), "Members of a front should be non dominate to each other.");
}
}
}
*/
var distinct = new Population();
foreach (var c in this.population[0])
{
bool same = false;
foreach (var fitnessParameter in this.properties.Objectives)
{
if (
distinct.Any(
c2 => Math.Abs(c2.Fitness[fitnessParameter] - c.Fitness[fitnessParameter]) < Epsilon))
{
same = true;
}
}
if (!same)
{
distinct.Add(c);
}
}
this.result.Cardinality = distinct.Count;
/*
ranks = distinct.GroupBy(g => g.Rank);
foreach (var rank in ranks)
{
foreach (var c1 in rank)
{
foreach (var c2 in rank)
{
Assert.That(!this.Dominates(c1, c2), "Members of a front should be non dominate to each other.");
}
}
}
*/
this.population[0] = this.population[1];
this.result.Population = (Population)this.population[0].Clone();
// foreach (var critter in this.Population)
// {
// this.result.AverageFitness += critter.Fitness;
// }
// this.result.AverageFitness /= population.Count;
// var sorted = this.Population.OrderBy(z => z.Fitness.TotalJourneyTime);
// this.result.MinimumFitness = sorted.First().Fitness;
// this.result.BestPath = sorted.First().Route;
// Tools.SavePopulation(this.population.GetRange(0, 25), ++this.generation, this.properties);
// this.BestNode = Tools.ToLinkedNodes(this.Population[0].Route);
// Console.WriteLine("Average fitness: {0}", this.result.MinimumFitness);
// return false;
}
/// <summary>
/// Returns a solution found using best-placement.
/// </summary>
/// <returns></returns>
protected override IRoute DoSolve(OsmSharp.Tools.Math.TSP.Problems.IProblem problem)
{
// create the settings.
SolverSettings settings = new SolverSettings(
-1,
-1,
1000000000,
-1,
-1,
-1);
Solver<List<int>, GeneticProblem, Fitness> solver =
new Solver<List<int>, GeneticProblem, Fitness>(
new GeneticProblem(problem),
settings,
null,
null,
null,
_generation_operation,
new FitnessCalculator(),
true, false);
Population<List<int>, GeneticProblem, Fitness> population =
new Population<List<int>, GeneticProblem, Fitness>(true);
while (population.Count < _population_size)
{
// generate new.
Individual<List<int>, GeneticProblem, Fitness> new_individual =
_generation_operation.Generate(solver);
// add to population.
population.Add(new_individual);
}
// select each individual once.
Population<List<int>, GeneticProblem, Fitness> new_population =
new Population<List<int>, GeneticProblem, Fitness>(true);
Individual<List<int>, GeneticProblem, Fitness> best = null;
int stagnation = 0;
while (stagnation < _stagnation)
{
while (new_population.Count < _population_size)
{
// select an individual and the next one.
int idx = OsmSharp.Tools.Math.Random.StaticRandomGenerator.Get().Generate(population.Count);
Individual<List<int>, GeneticProblem, Fitness> individual1 = population[idx];
Individual<List<int>, GeneticProblem, Fitness> individual2 = null;
if (idx == population.Count - 1)
{
individual2 = population[0];
}
else
{
individual2 = population[idx + 1];
}
population.RemoveAt(idx);
Individual<List<int>, GeneticProblem, Fitness> new_individual = _cross_over_operation.CrossOver(solver,
individual1, individual2);
new_individual.CalculateFitness(solver.Problem, solver.FitnessCalculator);
if (new_individual.Fitness.CompareTo(
individual1.Fitness) < 0)
{
new_population.Add(new_individual);
}
else
{
new_population.Add(individual1);
}
}
population = new_population;
population.Sort(solver, solver.FitnessCalculator);
new_population = new Population<List<int>, GeneticProblem, Fitness>(true);
if (best == null ||
best.Fitness.CompareTo(population[0].Fitness) > 0)
{
stagnation = 0;
best = population[0];
}
else
{
stagnation++;
}
//// report progress.
//OsmSharp.Tools.Output.OutputStreamHost.ReportProgress(stagnation,_stagnation,
// "OsmSharp.Tools.Math.TSP.EdgeAssemblyGenetic.EdgeAssemblyCrossOverSolver",
// "Solving using EAX...");
}
List<int> result = new List<int>(best.Genomes);
result.Insert(0, 0);
//return new SimpleAsymmetricRoute(result, true);
return DynamicAsymmetricRoute.CreateFrom(result);
}
