private void RunGA(){
_teams = LoadTeams();
_winners = _teams.FindAll(l => l.Winner == true);
_losers = _teams.FindAll(l => l.Winner == false);
const double crossoverProbability = 0.85;
const double mutationProbability = 0.15;
const int elitismPercentage = 5;
var population = new Population(1000, 72, true, false, ParentSelectionMethod.TournamentSelection);
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.DoublePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
var ga = new GeneticAlgorithm(population, EvaluateFitness);
ga.OnGenerationComplete += ga_OnGenerationComplete;
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
ga.Run(TerminateAlgorithm);
}
public void Run()
{
if (running)
{
return;
}
//create the elite operator
var elite = new Elite(ElitismPercentage);
//create the crossover operator
var crossover = new CrossoverIndex(CrossOverPercentage, ChromosomeUtils.NUMBER_GENES, true, GAF.Operators.CrossoverType.DoublePoint, ReplacementMethod.GenerationalReplacement);
//create the mutation operator
var mutate = new BinaryMutate(MutationPercentage);
//create the GA
var ga = new GeneticAlgorithm(population, fitness.CalculateFitness);
//add the operators
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutate);
ga.OnRunComplete += OnRunComplete;
//run the GA
running = true;
ga.Run(TerminateFunction);
}
public Genetics(Main form1, Func <string, int, string, bool> populateM)
{
pop = populateM;
form1.Text = "test";
form = form1;
var population = new Population(populationSize, 576, false, false);
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
ga = new GeneticAlgorithm(population, EvaluateFitness);
ga.OnGenerationComplete += ga_OnGenerationComplete;
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
}
private static void xMain(string[] args)
{
const double crossoverProbability = 0.85;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create a Population of 100 random chromosomes of length 44
var population = new Population(100, 44, false, false);
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, EvaluateFitness);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(TerminateAlgorithm);
}
// Using Genetic Algorithms
public static void RunThrowAndStick(this House house)
{
boundaryXDim = house.Boundary.XDim;
boundaryYDim = house.Boundary.YDim;
var population = new Population(populationSize, bitsToAdjustRoom * house.RoomCount, false, false);
//create the genetic operators
var elite = new Elite(5);
var crossover = new Crossover(0.85, true, CrossoverType.SinglePoint);
var mutation = new BinaryMutate(0.2, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, chromosome => EvaluateFitness(chromosome, house));
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
// Events subscription
ga.OnGenerationComplete += (sender, e) => WriteLine($"Fitness is {e.Population.GetTop(1)[0].Fitness}");
ga.OnRunComplete += (sender, e) => ga_OnRunComplete(sender, e, house);
// Run the GA
WriteLine("Starting the GA");
ga.Run(Terminate);
}
public void RunGA()
{
const double crossoverProbability = 0.8;
const double mutationProbability = 0.02;
const int elitismPercentage = 5;
//create the population
var population = new Population();
//create the chromosomes
foreach (User currUser in GA.currBoard.boardMembers)
{
var chromosome = new Chromosome();
chromosome.Genes.Add(new Gene(currUser));
chromosome.Genes.ShuffleFast();
population.Solutions.Add(chromosome);
}
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, CalculateFitness);
//subscribe to the GAs Generation Complete event
//ga.OnGenerationComplete += ga_OnGenerationComplete;
ga.OnRunComplete += ga_OnRunComplete;
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(TerminateAlgorithm);
}
private static void Main(string[] args)
{
const double crossoverProbability = 0.85;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create a Population of 100 random chromosomes of length 44
var population = new Population(100, 44, false, false);
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability);
//create the GA itself passing in the fitness function.
var ga = new GeneticAlgorithm(population, EvaluateFitness);
/* ------------------------------------------------------------------------------------
* if the fitness is defined in a separate assembly (see documentation),
* the following class can be used to load the assembly and access the
* fitness function
*
* var fitnessAssembly = new FitnessAssembly ("Example.IFitness.dll");
* var ga = new GeneticAlgorithm (population, fitnessAssembly.FitnessFunction);
* ------------------------------------------------------------------------------------*/
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(TerminateAlgorithm);
}
//En este metodo se configura los operadores y sus parametros para la corrida
public void correrAlgoritmoGenetico(ProgressBar progressBar, int cantPoblacion, int cantIteraciones)
{
reloj.Start();
this.progress = progressBar;
this.cantIteraciones = cantIteraciones;
this.fitnessRequired = 630;
var population = new Population(populationSize: cantPoblacion,
chromosomeLength: 63,
reEvaluateAll: true,
useLinearlyNormalisedFitness: true,
selectionMethod: ParentSelectionMethod.FitnessProportionateSelection);
var crossover = new Crossover(0.4)
{
AllowDuplicates = true,
CrossoverType = CrossoverType.SinglePoint
};
var binaryMutate = new BinaryMutate(mutationProbability: 0.05D, allowDuplicates: true);
var randomReplace = new RandomReplace(numberToReplace: 9, allowDuplicates: true);
var elite = new Elite(1);
var tempMutate = new MutacionPorTemperatura(0.01D, 0.3D, cantIteraciones, true);
var ga = new GeneticAlgorithm(population, CalculateFitness)
{
UseMemory = false
};
ga.OnGenerationComplete += ga_OnGenerationComplete;
ga.OnRunComplete += ga_OnRunComplete;
//aca se agregan los operadores para el algoritmo genetico
ga.Operators.Add(crossover);
//ga.Operators.Add(randomReplace);
ga.Operators.Add(tempMutate);
//ga.Operators.Add(binaryMutate);
ga.Operators.Add(elite);
ga.Run(Terminate);
}
static void ga()
{
vrange = new double[6];
vrange[1] = GAF.Math.GetRangeConstant(p.geo_amax - p.geo_amin, 10);
vrange[2] = GAF.Math.GetRangeConstant(p.geo_tmax - p.geo_tmin, 10);
vrange[3] = GAF.Math.GetRangeConstant(p.geo_bwmax - p.geo_bwmin, 10);
vrange[4] = GAF.Math.GetRangeConstant(p.geo_lmax - p.geo_lmin, 10);
vrange[5] = GAF.Math.GetRangeConstant(p.geo_nlmax - p.geo_nlmin, 10);
var pop = new Population(p.ga_pop, 50);
var elite = new Elite(Convert.ToInt32(p.ga_elit));
var crossover = new Crossover(p.ga_cros);
var mutacao = new BinaryMutate(p.ga_mut);
var ag = new GeneticAlgorithm(pop, fapt2d);
ag.Operators.Add(elite);
ag.Operators.Add(crossover);
ag.Operators.Add(mutacao);
ag.OnGenerationComplete += geracao2d;
Console.WriteLine("----------------------------------");
Console.WriteLine("Starting...");
Console.WriteLine("----------------------------------");
ag.Run(fim2d);
}
public static void Main(string[] args)
{
_ads = SetupAppDomain();
const double crossoverProbability = 0.65;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create the population
//var population = new Population(100, 44, false, false);
var population = new Population();
//create the chromosomes
for (var p = 0; p < 10; p++)
{
var chromosome = new Chromosome();
for (int i = 0; i < 2; i++)
{
ConfigVars v = new ConfigVars();
v.vars ["EMA_VAR1"] = RandomNumberBetweenInt(0, 20);
v.vars ["EMA_VAR2"] = RandomNumberBetweenInt(0, 100);
//v.vars ["LTD3"] = RandomNumberBetweenInt (0, 100);
//v.vars ["LTD4"] = RandomNumberBetweenInt (2, 200);
chromosome.Genes.Add(new Gene(v));
}
chromosome.Genes.ShuffleFast();
population.Solutions.Add(chromosome);
}
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, CalculateFitness);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
// ga.Operators.Add(elite);
// ga.Operators.Add(crossover);
// ga.Operators.Add(mutation);
var cv_operator = new ConfigVarsOperator();
ga.Operators.Add(cv_operator);
//run the GA
ga.Run(Terminate);
}
public void EvaluateProbe(List<IPosOnlyFeedback> test_probe_data, List<IPosOnlyFeedback> training_probe_data, List<IList<int>> test_users, List<IMapping> user_mapping,
List<IMapping> item_mapping,
int n = -1)
{
List<IList<int>> candidate_items = new List<IList<int>>();
List<RepeatedEvents> repeated_events = new List<RepeatedEvents>();
List<IBooleanMatrix> training_user_matrix = new List<IBooleanMatrix>();
List<IBooleanMatrix> test_user_matrix = new List<IBooleanMatrix>();
for (int i = 0; i < m_recommenders.Count; i++)
{
candidate_items.Add(new List<int>(test_probe_data[i].AllItems.Union(training_probe_data[i].AllItems)));
repeated_events.Add(RepeatedEvents.No);
if (candidate_items[i] == null)
throw new ArgumentNullException("candidate_items");
if (test_probe_data[i] == null)
test_users[i] = test_probe_data[i].AllUsers;
training_user_matrix.Add(training_probe_data[i].UserMatrix);
test_user_matrix.Add(test_probe_data[i].UserMatrix);
}
int num_users = 0;
var result = new ItemRecommendationEvaluationResults();
// make sure that the user matrix is completely initialized before entering parallel code
foreach (int user_id in test_users[0])
{
string original = user_mapping[0].ToOriginalID(user_id);
List<IList<Tuple<int, float>>> list_of_predictions = new List<IList<Tuple<int, float>>>();
HashSet<int> correct_items = new HashSet<int>();
List<HashSet<int>> ignore_items_for_this_user = new List<HashSet<int>>();
List<int> num_candidates_for_this_user = new List<int>();
correct_items = new HashSet<int>(test_user_matrix[0][user_id]);
correct_items.IntersectWith(candidate_items[0]);
for (int i = 0; i < m_recommenders.Count; i++)
{
int internalId = user_mapping[i].ToInternalID(original);
ignore_items_for_this_user.Add(new HashSet<int>(training_user_matrix[i][internalId]));
/* if (correct_items[i].Count == 0)
continue;
*/
ignore_items_for_this_user[i].IntersectWith(candidate_items[i]);
num_candidates_for_this_user.Add(candidate_items[i].Count - ignore_items_for_this_user[i].Count);
/*if (correct_items[i].Count == num_candidates_for_this_user[i])
continue;
*/
//Recomenda
var listaRecomendacao = m_recommenders[i].Recommend(user_id, candidate_items: candidate_items[i], n: n, ignore_items: ignore_items_for_this_user[i]);
for (int j = 0; j < listaRecomendacao.Count; j++)
{
string idOriginal = item_mapping[i].ToOriginalID(listaRecomendacao[j].Item1);
int idMappingZero = item_mapping[0].ToInternalID(idOriginal);
Tuple<int, float> tupla = new Tuple<int, float>(idMappingZero, listaRecomendacao[j].Item2);
listaRecomendacao[j] = tupla;
}
list_of_predictions.Add(listaRecomendacao);
}
//Usar o melhor
double maiorMapping = 0;
int idMaiorMapping = 0;
//Testar cada individual
for (int k = 0; k < list_of_predictions.Count; k++)
{
int[] prediction_probe = (from t in list_of_predictions[k] select t.Item1).ToArray();
double resultado = PrecisionAndRecall.AP(prediction_probe, correct_items);
if (resultado > maiorMapping)
{
maiorMapping = resultado;
idMaiorMapping = k;
}
}
//Set global so Fitness itens can see.
list_prediction_probes = list_of_predictions;
correct_items_global = correct_items;
//Algortimo Genetico
/* // Crossover = 80%
// Mutation = 5%
// Population size = 100
// Generations = 2000
// Genome size = 2
GA ga = new GA(0.8, 0.05, 40, 400, list_prediction_probes.Count);
ga.FitnessFunction = new GAFunction(Fitness);
//ga.FitnessFile = @"H:\fitness.csv";
ga.Elitism = true;
ga.Go();
double[] values;
double fitness;
ga.GetBest(out values, out fitness);*/
//create the GA using an initialised population and user defined Fitness Function
const double crossoverProbability = 0.85;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create a Population of random chromosomes of length 44
var population = new Population(40, list_of_predictions.Count * 10, false, false);
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.DoublePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, CalculateFitness);
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(Terminate);
var best = population.GetTop(1)[0];
double rangeConst = 1 / (System.Math.Pow(2, 10) - 1);
ga_weights[original] = new List<double>();
for (int i = 0; i < list_prediction_probes.Count; i++)
{
string str = best.ToBinaryString((i * 10), 10);
Int64 convertInt32 = Convert.ToInt32(str, 2);
double x = (convertInt32 * rangeConst);
ga_weights[original].Add(x);
}
best_alg[original] = idMaiorMapping;
num_users++;
if (num_users % 10 == 0)
Console.Error.Write(".");
if (num_users % 100 == 0)
Console.Error.WriteLine("");
}
}
public void StartLocating()
{
System.Data.DataTable dtStationCells = new System.Data.DataTable();
//Create XML Datat for first time
dtStationCells.TableName = "dt";
dtStationCells.Columns.Add("Index", typeof(int));
dtStationCells.Columns.Add("Name", typeof(string));
dtStationCells.Columns.Add("Weight", typeof(double));
dtStationCells.Columns.Add("Latitude", typeof(double));
dtStationCells.Columns.Add("Longitude", typeof(double));
dtStationCells.Columns.Add("RequestCount", typeof(int));
MovementProbilityMatrix = new Matrix(NumberOfCells, NumberOfCells);
Random rndi = new Random();
//CreateShiraz Map Cells
//29.5280603 29.57823449 29.72383715 29.69640358
//52.55447388 52.62863159 52.47344971 52.40478516
double latStart = 29.5280603, latEnd = 29.7238371, longStart = 52.4047851, longEnd = 52.6286315, currentLat = 0, currentLong = 0, latDistance = 0, longDistance = 0;
currentLat = latStart;
currentLong = longStart;
longDistance = (longEnd - longStart) / 2;
latDistance = (latEnd - latStart) / (NumberOfCells / 2);
for (int i = 0; i < NumberOfCells; i++)
{
int weight = rndi.Next(0, 100);
//dtStationCells.Rows.Add(i, i.ToString(), weight, rndi.Next(295280603, 297238371) / (double)100000000, rndi.Next(524047851, 526286315) / (double)100000000, weight);
dtStationCells.Rows.Add(i, i.ToString(), weight, currentLat, currentLong, weight);
currentLat += latDistance;
if (currentLat > latEnd)
{
currentLat = latStart;
}
currentLong += longDistance;
if (currentLong > longEnd)
{
currentLong = longStart;
}
}
dtStationCells.WriteXmlSchema(StationLocating.strRootResaultPath + "stationsSchema.xml");
dtStationCells.WriteXml(StationLocating.strRootResaultPath + "stations.xml");
//ReadPoints from Excel
//Microsoft.Office.Interop.Excel.Application xlApp1 = new Microsoft.Office.Interop.Excel.Application();
//Microsoft.Office.Interop.Excel.Workbook xlWorkBook1 = xlApp1.Workbooks.Open(Application.StartupPath + "\\Neighborhood_Labels.xls");
//Microsoft.Office.Interop.Excel.Worksheet xlSheet1 = xlWorkBook1.Sheets[1];
//Microsoft.Office.Interop.Excel.Range xlRange = xlSheet1.UsedRange;
//NumberOfCells = xlRange.Rows.Count;
//for (int i = 2; i <= xlRange.Rows.Count; i++)
//{
// if (xlRange.Cells[i, 1] != null && xlRange.Cells[i, 1].Value2 != null)
// dtStationCells.Rows.Add(xlRange.Cells[i, 3].Value2, rndi.Next(0, 100), Convert.ToDouble(xlRange.Cells[i, 2].Value2), Convert.ToDouble(xlRange.Cells[i, 1].Value2));
//}
//dtStationCells.WriteXmlSchema(@"c:\stationsSchema.xml");
//dtStationCells.WriteXml(@"c:\stations.xml");
//Create Movement Probility Matrix and Save it
MovementProbilityMatrix.FillMatrixWithEqalTotal();
//MovementProbilityMatrix.PrintMatrix();
//MovementProbilityMatrix.SaveMarixToJson("c:\\MovementProbilityMatrix.json");
// read Movement Probility Matrix from file
//MovementProbilityMatrix.ReadMatrixFromJson("c:\\MovementProbilityMatrix.json");
//Read stations data from XML file
//dtStationCells = new System.Data.DataTable();
//dtStationCells.ReadXmlSchema(@"c:\stationsSchema.xml");
//dtStationCells.ReadXml(@"c:\stations.xml");
StationCells = new List <BikeSharingStations.StationCell>();
for (int i = 0; i < dtStationCells.Rows.Count; i++)
{
StationCells.Add(new StationCell(Convert.ToInt32(dtStationCells.Rows[i]["Index"]), dtStationCells.Rows[i]["Name"].ToString(), (double)dtStationCells.Rows[i]["Weight"], (double)dtStationCells.Rows[i]["Latitude"], (double)dtStationCells.Rows[i]["Longitude"], Convert.ToInt32(dtStationCells.Rows[i]["RequestCount"])));
}
foreach (StationCell item in StationCells)
{
SumOfStationWeights += item.Weight;
}
const double crossoverProbability = 0.65;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
Microsoft.Office.Interop.Excel.Application xlApp = new Microsoft.Office.Interop.Excel.Application();;
Microsoft.Office.Interop.Excel.Workbook xlWorkBook = xlApp.Workbooks.Open(StationLocating.strRootResaultPath + "StationsData2.xls");;
object misValue = System.Reflection.Missing.Value;
Microsoft.Office.Interop.Excel.Worksheet xlSheetFinalData = xlWorkBook.Sheets[1];
for (RoundCount = 1; RoundCount <= 20; RoundCount++)
{
//create the population
var population = new Population();//NumberOfChoromosomes, NumberOfCells, false, false);
Random rndGeneIndex = new Random();
Random rnd = new Random();
//create the chromosomes;
for (var p = 0; p < NumberOfChoromosomes; p++)
{
var chromosome = new Chromosome();
int NumberOfBikesInThisChoromosome = 0;
for (var g = 0; g < NumberOfCells; g++)
{
chromosome.Genes.Add(new Gene(0));
}
//int geneIndex = 0;
while (NumberOfBikesInThisChoromosome < MaximumNumberOfBikesInSystem)
{
int temp = rnd.Next(MaximumNumberOfbikeInEachStation / 2, MaximumNumberOfbikeInEachStation);
if (NumberOfBikesInThisChoromosome + temp > MaximumNumberOfBikesInSystem)
{
temp = MaximumNumberOfBikesInSystem - NumberOfBikesInThisChoromosome;
}
NumberOfBikesInThisChoromosome += temp;
chromosome.Genes[rndGeneIndex.Next(0, NumberOfCells)] = new Gene(temp);
//chromosome.Genes.Add(new Gene(temp));
//geneIndex++;
}
//chromosome.Genes.ShuffleFast();
int sumBikes = 0;
for (int i = 0; i < chromosome.Count; i++)
{
sumBikes += Convert.ToInt32(chromosome.Genes[i].RealValue);
}
if (sumBikes > MaximumNumberOfBikesInSystem)
{
sumBikes = 0;
}
population.Solutions.Add(chromosome);
}
//create the chromosomes;
//for (var p = 0; p < NumberOfChoromosomes; p++)
//{
// var chromosome = new Chromosome();
// for (var g = 0; g < NumberOfCells; g++)
// {
// chromosome.Genes.Add(new Gene(rnd.Next(0, MaximumNumberOfbikeInEachStation)));
// }
// chromosome.Genes.ShuffleFast();
// population.Solutions.Add(chromosome);
//}
//create the genetic operators
var elite = new Elite(elitismPercentage);
//create the crossover operator
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
//crossover.ReplacementMethod = ReplacementMethod.DeleteLast;
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, EvaluateFitness);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(TerminateAlgorithm);
Console.WriteLine("intFitnessCount : " + intFitnessCount.ToString());
Console.WriteLine("intWrongFitnessCount : " + intWrongFitnessCount.ToString());
//Export Resault to excel
Microsoft.Office.Interop.Excel.Worksheet xlStationsDataSheet;
xlWorkBook.Worksheets.Add();
xlStationsDataSheet = (Microsoft.Office.Interop.Excel.Worksheet)xlWorkBook.Worksheets.get_Item(1);
int excelColumnIndex = 1;
xlStationsDataSheet.Name = string.Format("Round{0:00}", RoundCount);
xlStationsDataSheet.Cells[1, excelColumnIndex] = "CellIndex"; // Index
xlStationsDataSheet.Cells[2, excelColumnIndex] = "StationName"; // Name
xlStationsDataSheet.Cells[3, excelColumnIndex] = "Latitude"; // Latitude
xlStationsDataSheet.Cells[4, excelColumnIndex] = "Longitude"; // Longitude
xlStationsDataSheet.Cells[5, excelColumnIndex] = "Weight"; // Weight
xlStationsDataSheet.Cells[6, excelColumnIndex] = "NumberOfBikes"; // NumberOfBikes
xlStationsDataSheet.Cells[7, excelColumnIndex] = "Station capacity"; // Station capacity
xlStationsDataSheet.Cells[8, excelColumnIndex] = "Total Distance"; // Avrage Of Accessibility
xlStationsDataSheet.Cells[9, excelColumnIndex] = "Number of Bikes for Rebalancing"; // Number of Bikes for Rebalancing
excelColumnIndex += 1;
var Topchromosome = population.Solutions[0];
ComputeAccessibility(Topchromosome);
ComputeRebalancingCost(Topchromosome);
SaveProbilityMartix(string.Format(StationLocating.strRootResaultPath + "ProbibilityMatrix-Round{0:00}.json", RoundCount), Topchromosome);
double totalbikeinthissystem = 0;
for (int i = 0; i < Topchromosome.Count; i++)
{
if (Topchromosome.Genes[i].RealValue != 0)
{
xlStationsDataSheet.Cells[1, excelColumnIndex] = StationCells[i].Index; // Index
xlStationsDataSheet.Cells[2, excelColumnIndex] = StationCells[i].Name; // Name
xlStationsDataSheet.Cells[3, excelColumnIndex] = StationCells[i].Latitude; // Latitude
xlStationsDataSheet.Cells[4, excelColumnIndex] = StationCells[i].Longitude; // Longitude
xlStationsDataSheet.Cells[5, excelColumnIndex] = StationCells[i].Weight; // Weight
xlStationsDataSheet.Cells[6, excelColumnIndex] = StationCells[i].NumberOfBikes; // NumberOfBikes
xlStationsDataSheet.Cells[7, excelColumnIndex] = Topchromosome.Genes[i].RealValue; // Station capacity
xlStationsDataSheet.Cells[8, excelColumnIndex] = TotalDistance; // Avrage Of Accessibility
xlStationsDataSheet.Cells[9, excelColumnIndex] = NumberOfBikesToBeRebalance; // Number of Bikes for Rebalancing
totalbikeinthissystem += Topchromosome.Genes[i].RealValue;
excelColumnIndex++;
}
}
xlSheetFinalData.Cells[1, 1] = "Round Count";
xlSheetFinalData.Cells[1, 2] = "TotalDistance";
xlSheetFinalData.Cells[1, 3] = "NumberOfBikesToBeRebalance";
xlSheetFinalData.Cells[1, 4] = "Number of Bikes in system";
xlSheetFinalData.Cells[1, 5] = "Number of stations";
xlSheetFinalData.Cells[1, 6] = "Rebalancing is Computed";
xlSheetFinalData.Cells[1, 7] = "fitnessValue";
xlSheetFinalData.Cells[1, 8] = "AccessibilityFitness";
xlSheetFinalData.Cells[1, 9] = "RebalancingFitness";
xlSheetFinalData.Cells[RoundCount + 1, 1] = RoundCount;
xlSheetFinalData.Cells[RoundCount + 1, 2] = TotalDistance;
xlSheetFinalData.Cells[RoundCount + 1, 3] = NumberOfBikesToBeRebalance; // Number of Bikes for Rebalancing
xlSheetFinalData.Cells[RoundCount + 1, 4] = totalbikeinthissystem; // Number of Bikes in system
xlSheetFinalData.Cells[RoundCount + 1, 5] = excelColumnIndex - 2; // Number of stations
xlSheetFinalData.Cells[RoundCount + 1, 6] = (RoundCount % 2 == 0) ? "True" : "False";
xlSheetFinalData.Cells[RoundCount + 1, 7] = fitnessValue; // Number of Bikes for Rebalancing
xlSheetFinalData.Cells[RoundCount + 1, 8] = AccessibilityFitness; // Number of Bikes for Rebalancing
xlSheetFinalData.Cells[RoundCount + 1, 9] = (RoundCount % 2 == 0)? RebalancingFitness : 0; // Number of Bikes for Rebalancing
releaseObject(xlStationsDataSheet);
xlWorkBook.Save();
//xlWorkBook.SaveAs(string.Format("c:\\StationsData.xls"), Microsoft.Office.Interop.Excel.XlFileFormat.xlWorkbookNormal, misValue, misValue, misValue, misValue, Microsoft.Office.Interop.Excel.XlSaveAsAccessMode.xlExclusive, misValue, misValue, misValue, misValue, misValue);
}
xlWorkBook.SaveAs(string.Format(StationLocating.strRootResaultPath + "StationsData.xls"), Microsoft.Office.Interop.Excel.XlFileFormat.xlWorkbookNormal, misValue, misValue, misValue, misValue, Microsoft.Office.Interop.Excel.XlSaveAsAccessMode.xlExclusive, misValue, misValue, misValue, misValue, misValue);
releaseObject(xlSheetFinalData);
releaseObject(xlWorkBook);
releaseObject(xlApp);
xlWorkBook.Close(true, misValue, misValue);
xlApp.Quit();
MessageBox.Show("Excel file created , you can find the file " + StationLocating.strRootResaultPath);
}
public void EvaluateProbe(List <IPosOnlyFeedback> test_probe_data, List <IPosOnlyFeedback> training_probe_data, List <IList <int> > test_users, List <IMapping> user_mapping,
List <IMapping> item_mapping,
int n = -1)
{
List <IList <int> > candidate_items = new List <IList <int> >();
List <RepeatedEvents> repeated_events = new List <RepeatedEvents>();
List <IBooleanMatrix> training_user_matrix = new List <IBooleanMatrix>();
List <IBooleanMatrix> test_user_matrix = new List <IBooleanMatrix>();
for (int i = 0; i < m_recommenders.Count; i++)
{
candidate_items.Add(new List <int>(test_probe_data[i].AllItems.Union(training_probe_data[i].AllItems)));
repeated_events.Add(RepeatedEvents.No);
if (candidate_items[i] == null)
{
throw new ArgumentNullException("candidate_items");
}
if (test_probe_data[i] == null)
{
test_users[i] = test_probe_data[i].AllUsers;
}
training_user_matrix.Add(training_probe_data[i].UserMatrix);
test_user_matrix.Add(test_probe_data[i].UserMatrix);
}
int num_users = 0;
var result = new ItemRecommendationEvaluationResults();
// make sure that the user matrix is completely initialized before entering parallel code
foreach (int user_id in test_users[0])
{
string original = user_mapping[0].ToOriginalID(user_id);
List <IList <Tuple <int, float> > > list_of_predictions = new List <IList <Tuple <int, float> > >();
HashSet <int> correct_items = new HashSet <int>();
List <HashSet <int> > ignore_items_for_this_user = new List <HashSet <int> >();
List <int> num_candidates_for_this_user = new List <int>();
correct_items = new HashSet <int>(test_user_matrix[0][user_id]);
correct_items.IntersectWith(candidate_items[0]);
for (int i = 0; i < m_recommenders.Count; i++)
{
int internalId = user_mapping[i].ToInternalID(original);
ignore_items_for_this_user.Add(new HashSet <int>(training_user_matrix[i][internalId]));
/* if (correct_items[i].Count == 0)
* continue;
*/
ignore_items_for_this_user[i].IntersectWith(candidate_items[i]);
num_candidates_for_this_user.Add(candidate_items[i].Count - ignore_items_for_this_user[i].Count);
/*if (correct_items[i].Count == num_candidates_for_this_user[i])
* continue;
*/
//Recomenda
var listaRecomendacao = m_recommenders[i].Recommend(user_id, candidate_items: candidate_items[i], n: n, ignore_items: ignore_items_for_this_user[i]);
for (int j = 0; j < listaRecomendacao.Count; j++)
{
string idOriginal = item_mapping[i].ToOriginalID(listaRecomendacao[j].Item1);
int idMappingZero = item_mapping[0].ToInternalID(idOriginal);
Tuple <int, float> tupla = new Tuple <int, float>(idMappingZero, listaRecomendacao[j].Item2);
listaRecomendacao[j] = tupla;
}
list_of_predictions.Add(listaRecomendacao);
}
//Usar o melhor
double maiorMapping = 0;
int idMaiorMapping = 0;
//Testar cada individual
for (int k = 0; k < list_of_predictions.Count; k++)
{
int[] prediction_probe = (from t in list_of_predictions[k] select t.Item1).ToArray();
double resultado = PrecisionAndRecall.AP(prediction_probe, correct_items);
if (resultado > maiorMapping)
{
maiorMapping = resultado;
idMaiorMapping = k;
}
}
//Set global so Fitness itens can see.
list_prediction_probes = list_of_predictions;
correct_items_global = correct_items;
//Algortimo Genetico
/* // Crossover = 80%
* // Mutation = 5%
* // Population size = 100
* // Generations = 2000
* // Genome size = 2
* GA ga = new GA(0.8, 0.05, 40, 400, list_prediction_probes.Count);
*
* ga.FitnessFunction = new GAFunction(Fitness);
*
* //ga.FitnessFile = @"H:\fitness.csv";
* ga.Elitism = true;
* ga.Go();
*
* double[] values;
* double fitness;
* ga.GetBest(out values, out fitness);*/
//create the GA using an initialised population and user defined Fitness Function
const double crossoverProbability = 0.85;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create a Population of random chromosomes of length 44
var population = new Population(40, list_of_predictions.Count * 10, false, false);
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.DoublePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, CalculateFitness);
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(Terminate);
var best = population.GetTop(1)[0];
double rangeConst = 1 / (System.Math.Pow(2, 10) - 1);
ga_weights[original] = new List <double>();
for (int i = 0; i < list_prediction_probes.Count; i++)
{
string str = best.ToBinaryString((i * 10), 10);
Int64 convertInt32 = Convert.ToInt32(str, 2);
double x = (convertInt32 * rangeConst);
ga_weights[original].Add(x);
}
best_alg[original] = idMaiorMapping;
num_users++;
if (num_users % 10 == 0)
{
Console.Error.Write(".");
}
if (num_users % 100 == 0)
{
Console.Error.WriteLine("");
}
}
}
private static void Main(string[] args)
{
Console.Title = "TMNF Ga AI";
Console.WriteLine("__________________TMNF AI GA__________________");
Console.Write($"MaxTime for each chromosome (enter for default: {maxTime}): ");
string maxTimeInput = Console.ReadLine();
if (!(maxTimeInput.ToString() == ""))
{
maxTime = Convert.ToInt32(maxTimeInput);
Console.WriteLine("Maxtime set to " + maxTime);
}
Console.Write($"Cheat engine speed cheat (enter for no speed hack): ");
string cheatEngineSpeedInput = Console.ReadLine();
if (!(cheatEngineSpeedInput.ToString() == ""))
{
cheatEngineSpeed = Convert.ToInt32(cheatEngineSpeedInput);
}
Console.Write($"Population number |must be even| (enter for default {populationCount}): ");
string populationInput = Console.ReadLine();
if (!(populationInput.ToString() == ""))
{
populationCount = Convert.ToInt32(populationInput);
int mod = populationCount % 2;
populationCount += mod;
Console.WriteLine("Population set to: " + populationCount);
}
Console.Write($"Interval - ms between actions (enter for default {intervalTime}): ");
string intervalTimeInput = Console.ReadLine();
if (!(intervalTimeInput.ToString() == ""))
{
intervalTime = Convert.ToInt32(intervalTimeInput);
}
Console.WriteLine("Focus trackmania window now");
//Sleep 4000 ms for at få tid til at klikke ind på Trackmania.
Thread.Sleep(4000);
Console.WriteLine("Starting program");
//Angiv probabilities af crossover/mutation og procentdelen af population der kan være elite(eligible for crossover)
const double crossoverProbability = 1;
const double mutationProbability = 0.08;
const int elitismPercentage = 10;
Console.WriteLine($"{maxTime}, {intervalTime}");
int chroLengths = ((maxTime * 1000) * 4) / intervalTime;
Console.WriteLine("Calculated chromosome length: " + chroLengths);
int test = chroLengths % 4;
chroLengths += test;
Console.WriteLine("Adapted Length: " + chroLengths);
//create a Population with ? random chromosomes of length ?
//First generation/population will have double the amount automatically for better results
var population = new Population(populationCount, chroLengths, false, false);
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, EvaluateFitness);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(TerminateAlgorithm);
}
public List <Star> Solve()
{
const double crossoverProbability = 0.75;
const double mutationProbability = 0.5;
const int elitismPercentage = 5;
var population = new Population();
//create the chromosomes
for (var p = 0; p < 100; p++)
{
var chromosome = new Chromosome();
for (var g = 0; g < GlobalVariables.Comparables - _elitestars.Count; g++)
{
chromosome.Genes.Add(new Gene(_rnd.NextDouble()));
}
population.Solutions.Add(chromosome);
}
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, EvaluateQuality);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(TerminateAlgorithm);
List <Star> picks = new List <Star>();
picks.AddRange(_elitestars);
foreach (Gene gene in population.GetTop(1)[0].Genes)
{
picks.Add(_eligiblestars[(int)(System.Math.Abs(gene.RealValue) * _eligiblestars.Count)]);
}
FastReduction();
var mag = _teststars
.Select(x => (x.VMag - x.CatalogEntry.Vmag));
Console.WriteLine();
Console.WriteLine("Best fit mean : " + mag.Average());
Console.WriteLine("Best fit deviation: " + mag.StdDev());
picks = picks.Distinct(new StarComparer()).ToList();
return(picks);
}
public static void Main(string[] args)
{
//Initialize:
string mode = "RELEASE";
var liveMode = Config.GetBool("live-mode");
#if DEBUG
mode = "DEBUG";
#endif
// Console.WriteLine("Running " + algorithm + "...");
Config.Set("live-mode", "false");
Config.Set("messaging-handler", "QuantConnect.Messaging.Messaging");
Config.Set("job-queue-handler", "QuantConnect.Queues.JobQueue");
Config.Set("api-handler", "QuantConnect.Api.Api");
//Config.Set("result-handler", "QuantConnect.Lean.Engine.Results.OptimizationResultHandler");
Config.Set("result-handler", "QuantConnect.Lean.Engine.Results.ConsoleResultHandler");
Config.Set("EMA_VAR1", "10");
_ads = SetupAppDomain();
//rc = new RunClass();
const double crossoverProbability = 0.65;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create the population
//var population = new Population(100, 44, false, false);
var population = new Population();
//create the chromosomes
for (var p = 0; p < 100; p++)
{
var chromosome = new Chromosome();
for (int i = 0; i < 100; i++)
chromosome.Genes.Add(new Gene(i));
chromosome.Genes.ShuffleFast();
population.Solutions.Add(chromosome);
}
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, CalculateFitness);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(Terminate);
}
public static void Main(string[] args)
{
_ads = SetupAppDomain ();
const double crossoverProbability = 0.65;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create the population
//var population = new Population(100, 44, false, false);
var population = new Population();
//create the chromosomes
for (var p = 0; p < 10; p++)
{
var chromosome = new Chromosome();
for (int i = 0; i < 2; i++) {
ConfigVars v = new ConfigVars ();
v.vars ["EMA_VAR1"] = RandomNumberBetweenInt (0, 20);
v.vars ["EMA_VAR2"] = RandomNumberBetweenInt (0, 100);
//v.vars ["LTD3"] = RandomNumberBetweenInt (0, 100);
//v.vars ["LTD4"] = RandomNumberBetweenInt (2, 200);
chromosome.Genes.Add (new Gene (v));
}
chromosome.Genes.ShuffleFast();
population.Solutions.Add(chromosome);
}
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, CalculateFitness);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
// ga.Operators.Add(elite);
// ga.Operators.Add(crossover);
// ga.Operators.Add(mutation);
var cv_operator = new ConfigVarsOperator ();
ga.Operators.Add (cv_operator);
//run the GA
ga.Run(Terminate);
}
internal void Run(AreaManager areaManager, Population conv, Population inno, Population obj)
{
if (running)
{
return;
}
cells = originalMap.SpawnCells;
Chromosome chrom = ChromosomeUtils.ChromosomeFromMap(originalMap);
string binaryString = chrom.ToBinaryString();
convergenceFitness = new ConvergenceFitness(cells, binaryString);
guidelineFitness = new Guideline(cells, areaManager, MaxMonsters, MaxItens, HordesPercentage)
{
MaxItemsLever = LeverMaxItem,
MaxMonstersLever = LeverMaxMonster,
AmountHordesLever = LeverAmountHordes,
DangerLever = LeverDanger,
AccessibilityLever = LeverAccessibility
};
double total = GuidelinePercentage + UserPercentage + InnovationPercentage;
guidelineLevel = GuidelinePercentage / total;
ConvergenceLevel = UserPercentage / total;
innovationLevel = InnovationPercentage / total;
//we can create an empty population as we will be creating the
//initial solutions manually.
var population = new Population(InitialPopulation, cells.Count * ChromosomeUtils.NUMBER_GENES, true, true, ParentSelectionMethod.StochasticUniversalSampling);
population.Solutions.Clear();
population.Solutions.AddRange(obj.GetTop(10));
population.Solutions.AddRange(inno.GetTop(10));
population.Solutions.AddRange(conv.GetTop(10));
//create the elite operator
var elite = new Elite(ElitismPercentage);
//create the crossover operator
var crossover = new CrossoverIndex(CrossOverPercentage, ChromosomeUtils.NUMBER_GENES, true, GAF.Operators.CrossoverType.DoublePoint, ReplacementMethod.GenerationalReplacement);
//create the mutation operator
var mutate = new BinaryMutate(MutationPercentage);
//create the GA
var ga = new GeneticAlgorithm(population, CalculateFitness);
//add the operators
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
//ga.Operators.Add(mutate);
ga.OnRunComplete += OnRunComplete;
//run the GA
running = true;
ga.Run(TerminateFunction);
}
static void Main(string[] args)
{
knapsackItems = new List <Bag.Item>();
knapsackItems.Add(new Bag.Item()
{
Name = "Map", Weight = 9, Value = 150
});
knapsackItems.Add(new Bag.Item()
{
Name = "Water", Weight = 153, Value = 200
});
knapsackItems.Add(new Bag.Item()
{
Name = "Compass", Weight = 13, Value = 35
});
knapsackItems.Add(new Bag.Item()
{
Name = "Sandwitch", Weight = 50, Value = 160
});
knapsackItems.Add(new Bag.Item()
{
Name = "Glucose", Weight = 15, Value = 60
});
knapsackItems.Add(new Bag.Item()
{
Name = "Tin", Weight = 68, Value = 45
});
knapsackItems.Add(new Bag.Item()
{
Name = "Banana", Weight = 27, Value = 60
});
knapsackItems.Add(new Bag.Item()
{
Name = "Apple", Weight = 39, Value = 40
});
knapsackItems.Add(new Bag.Item()
{
Name = "Cheese", Weight = 23, Value = 30
});
knapsackItems.Add(new Bag.Item()
{
Name = "Beer", Weight = 52, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "Suntan Cream", Weight = 11, Value = 70
});
knapsackItems.Add(new Bag.Item()
{
Name = "Camera", Weight = 32, Value = 30
});
knapsackItems.Add(new Bag.Item()
{
Name = "T-shirt", Weight = 24, Value = 15
});
knapsackItems.Add(new Bag.Item()
{
Name = "Trousers", Weight = 48, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "Umbrella", Weight = 73, Value = 40
});
knapsackItems.Add(new Bag.Item()
{
Name = "WaterProof Trousers", Weight = 42, Value = 70
});
knapsackItems.Add(new Bag.Item()
{
Name = "Note-Case", Weight = 22, Value = 80
});
knapsackItems.Add(new Bag.Item()
{
Name = "Sunglasses", Weight = 7, Value = 20
});
knapsackItems.Add(new Bag.Item()
{
Name = "Towel", Weight = 18, Value = 12
});
knapsackItems.Add(new Bag.Item()
{
Name = "Socks", Weight = 4, Value = 50
});
knapsackItems.Add(new Bag.Item()
{
Name = "Book", Weight = 30, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "waterproof overclothes ", Weight = 43, Value = 75
});
const double crossoverProbability = 0.65;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create the population
var population = new Population(100, 22, false, false);
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, EvaluateFitness);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(TerminateAlgorithm);
//get the best solution and print Items
var Bestchromosome = ga.Population.GetTop(1)[0];
//decode chromosome
Console.WriteLine("Maximum value of knapsack contains these Items : ");
Bag BestBag = new Bag();
for (int i = 0; i < Bestchromosome.Count; i++)
{
if (Bestchromosome.Genes[i].BinaryValue == 1)
{
Console.WriteLine(knapsackItems[i].ToString());
BestBag.AddItem(knapsackItems[i]);
}
}
Console.WriteLine(" Best knapsack information:\n Total Weight : {0} Total Value : {1} Fitness : {2}", BestBag.TotalWeight, BestBag.TotalValue, Bestchromosome.Fitness);
Console.ReadKey();
}
static void Main(string[] args)
{
knapsackItems = new List <Bag.Item>();
knapsackItems.Add(new Bag.Item()
{
Name = "a", Weight = 9, Value = 150
});
knapsackItems.Add(new Bag.Item()
{
Name = "b", Weight = 153, Value = 200
});
knapsackItems.Add(new Bag.Item()
{
Name = "c", Weight = 13, Value = 35
});
knapsackItems.Add(new Bag.Item()
{
Name = "d", Weight = 50, Value = 160
});
knapsackItems.Add(new Bag.Item()
{
Name = "e", Weight = 15, Value = 60
});
knapsackItems.Add(new Bag.Item()
{
Name = "f", Weight = 68, Value = 45
});
knapsackItems.Add(new Bag.Item()
{
Name = "g", Weight = 27, Value = 60
});
knapsackItems.Add(new Bag.Item()
{
Name = "h", Weight = 39, Value = 40
});
knapsackItems.Add(new Bag.Item()
{
Name = "i", Weight = 23, Value = 30
});
knapsackItems.Add(new Bag.Item()
{
Name = "j", Weight = 52, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "k", Weight = 11, Value = 70
});
knapsackItems.Add(new Bag.Item()
{
Name = "l", Weight = 32, Value = 30
});
knapsackItems.Add(new Bag.Item()
{
Name = "m", Weight = 24, Value = 15
});
knapsackItems.Add(new Bag.Item()
{
Name = "n", Weight = 48, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "o", Weight = 73, Value = 40
});
knapsackItems.Add(new Bag.Item()
{
Name = "p", Weight = 42, Value = 70
});
knapsackItems.Add(new Bag.Item()
{
Name = "q", Weight = 22, Value = 80
});
knapsackItems.Add(new Bag.Item()
{
Name = "r", Weight = 7, Value = 20
});
knapsackItems.Add(new Bag.Item()
{
Name = "s", Weight = 18, Value = 12
});
knapsackItems.Add(new Bag.Item()
{
Name = "t", Weight = 4, Value = 50
});
knapsackItems.Add(new Bag.Item()
{
Name = "u", Weight = 30, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "v", Weight = 43, Value = 75
});
knapsackItems.Add(new Bag.Item()
{
Name = "v", Weight = 43, Value = 75
});
knapsackItems.Add(new Bag.Item()
{
Name = "v", Weight = 43, Value = 75
});
knapsackItems.Add(new Bag.Item()
{
Name = "v", Weight = 43, Value = 75
});
const double crossoverProbability = 0.65;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
// tao quan the
var population = new Population(100, 22, false, false);
// tao toan tu di truyen
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
// tao GA
var ga = new GeneticAlgorithm(population, EvaluateFitness);
ga.OnGenerationComplete += ga_OnGenerationComplete;
// them tung toan tu vao GA
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
// chay GA
ga.Run(TerminateAlgorithm);
// lay do vat tot nhat
var Bestchromosome = ga.Population.GetTop(1)[0];
// decode chromosome
Console.WriteLine("Maximum value of knapsack contains these Items : ");
Bag BestBag = new Bag();
for (int i = 0; i < Bestchromosome.Count; i++)
{
if (Bestchromosome.Genes[i].BinaryValue == 1)
{
Console.WriteLine(knapsackItems[i].ToString());
BestBag.AddItem(knapsackItems[i]);
}
}
Console.WriteLine(" Best knapsack information:\n Total Weight : {0} Total Value : {1} Fitness : {2}", BestBag.TotalWeight, BestBag.TotalValue, Bestchromosome.Fitness);
Console.ReadKey();
}
