public void CanCrossover()
{
var listLeft = new List <IGenoTypeNode>
{
new SquareRoot(),
new Multiplication(),
new Plus(),
new Minus(),
new FeatureTerminal("a"),
new FeatureTerminal("b"),
new FeatureTerminal("c"),
new FeatureTerminal("d")
};
var listRight = new List <IGenoTypeNode>
{
new Sinus(),
new Multiplication(),
new Division(),
new Minus(),
new FeatureTerminal("a1"),
new FeatureTerminal("b1"),
new FeatureTerminal("c1"),
new FeatureTerminal("d1")
};
var possibleFunctions = new List <IGenoTypeNode>
{
new SquareRoot(),
new Multiplication(),
new Plus(),
new Minus()
};
var possibleTerminals = new List <IGenoTypeNode>
{
new FeatureTerminal("a"),
new FeatureTerminal("b"),
new FeatureTerminal("c"),
new FeatureTerminal("d")
};
var eaGeneExpressionParameters =
new EaGeneExpressionParameters(4, possibleFunctions, possibleTerminals)
{
CrossoverProbability = 1.0
};
IGenoType left = new GeneExpression.GenoType {
GenoTypeNodes = listLeft
};
IGenoType right = new GeneExpression.GenoType {
GenoTypeNodes = listRight
};
var randomGenerator = new UniformRandomGenerator();
var genotypeCrossoverator1 = new GenoTypeCrossoveratorBasic1(randomGenerator, eaGeneExpressionParameters);
var phenoTypeTreeLeft1 = new GeneExpression.PhenoTypeTree(left.GenoTypeNodes);
var phenoTypeTreeRight1 = new GeneExpression.PhenoTypeTree(right.GenoTypeNodes);
genotypeCrossoverator1.PerformCrossover(ref left, ref right);
var phenoTypeTreeLeft2 = new GeneExpression.PhenoTypeTree(left.GenoTypeNodes);
var phenoTypeTreeRight2 = new GeneExpression.PhenoTypeTree(right.GenoTypeNodes);
Assert.NotEqual(phenoTypeTreeLeft1, phenoTypeTreeLeft2);
Assert.NotEqual(phenoTypeTreeRight1, phenoTypeTreeRight2);
}
static void Main()
{
var randomGenerator = new UniformRandomGenerator();
var dataSet = GetDataSet();
var target = GetTarget();
var eaGeneExpressionParameters = GetEaGeneExpressionParameters(dataSet);
var parameterTerminalFactory = new ParameterTerminalFactory(eaGeneExpressionParameters, randomGenerator);
var genoTypeFactory = new GenoTypeFactory(eaGeneExpressionParameters, randomGenerator, parameterTerminalFactory);
var mutator = new GenoTypeMutatorBasic1(randomGenerator, eaGeneExpressionParameters, genoTypeFactory);
var crossOverator = new GenoTypeCrossoveratorBasic1(randomGenerator, eaGeneExpressionParameters);
var populationP = GetFirstPopulation(eaGeneExpressionParameters, parameterTerminalFactory, genoTypeFactory);
var listOfObjectiveValuesP = new List <IObjectiveValues>();
for (var c = 0; c < populationP.Count; c++)
{
var objectiveValues = GetObjectiveValues(target, dataSet, populationP[c]);
listOfObjectiveValuesP.Add(objectiveValues);
}
listOfObjectiveValuesP = Nsga2TournamentSelector.PerformSelection(eaGeneExpressionParameters.TournamentSize, listOfObjectiveValuesP, randomGenerator);
var tempPopulation = new List <Individual>();
foreach (var objectiveValues in listOfObjectiveValuesP)
{
var indy = populationP.FirstOrDefault(x => x.Guid == objectiveValues.IndividualGuid);
tempPopulation.Add((Individual)indy.Clone());
}
populationP = tempPopulation;
for (var generation = 0; generation < eaGeneExpressionParameters.NumberOfGeneration; generation++)
{
var populationQ = new List <Individual>();
foreach (var individual in populationP)
{
populationQ.Add((Individual)individual.Clone());
}
populationQ = PerformCrossOver(populationQ, randomGenerator, crossOverator);
foreach (var individual in populationQ)
{
mutator.PerformMutation(ref individual.GenoType);
}
var combinedPopulation = new List <Individual>();
var combinedlistOfObjectiveValues = new List <IObjectiveValues>();
foreach (var individual in populationP)
{
var clone = (Individual)individual.Clone();
combinedPopulation.Add(clone);
var objectiveValues = GetObjectiveValues(target, dataSet, clone);
combinedlistOfObjectiveValues.Add(objectiveValues);
}
foreach (var individual in populationQ)
{
var clone = (Individual)individual.Clone();
combinedPopulation.Add(clone);
var objectiveValues = GetObjectiveValues(target, dataSet, clone);
combinedlistOfObjectiveValues.Add(objectiveValues);
}
combinedlistOfObjectiveValues = Nsga2Ranker.Rank(combinedlistOfObjectiveValues);
combinedlistOfObjectiveValues = Nsga2Crowder.CalculateCrowdingDistances(combinedlistOfObjectiveValues);
combinedlistOfObjectiveValues = combinedlistOfObjectiveValues.OrderBy(i => i.Rank).ThenByDescending(i => i.CrowdingDistance).ToList();
tempPopulation = new List <Individual>();
var counter = 0;
var smallestMse = decimal.MaxValue;
decimal largestMse = 0;
var smallestPosition = -1;
foreach (var objectiveValues in combinedlistOfObjectiveValues)
{
var indy = combinedPopulation.FirstOrDefault(x => x.Guid == objectiveValues.IndividualGuid);
tempPopulation.Add((Individual)indy.Clone());
var ovs = GetObjectiveValues(target, dataSet, combinedPopulation.FirstOrDefault(x => x.Guid == objectiveValues.IndividualGuid));
if (ovs.Values[0] < smallestMse)
{
smallestMse = ovs.Values[0];
smallestPosition = counter;
}
if (ovs.Values[0] > largestMse)
{
largestMse = ovs.Values[0];
}
counter++;
if (counter == eaGeneExpressionParameters.PopulationSize)
{
break;
}
}
populationP = tempPopulation;
Console.WriteLine(generation + " " + smallestMse + " " + largestMse);
Console.WriteLine(new PhenoTypeTree(populationP[smallestPosition].GenoType.GenoTypeNodes));
}
}
