void InitialiseAgents(int MinMaxFlag, double[,] Bounds, ParameterTypes[] VariableType)
{
Agents = new double[PopulationSize, Dimensionality];
Scores = new double[PopulationSize];
SobolRNG initialisationRNG = new SobolRNG(Dimensionality, 777);
double[] randoms;
for (int i = 0; i < PopulationSize; ++i)
{
while (true)
{
randoms = initialisationRNG.Next();
for (int j = 0; j < Dimensionality; ++j)
{
Agents[i, j] = AdjustValueForVariableType(VariableType[j], randoms[j] * Bounds[j, 0] + (1 - randoms[j]) * Bounds[j, 1]);
}
if (!PreviousParameterSets.ContainsKey(Agents.Row(i)))
{
PreviousParameterSets.Add(Agents.Row(i), true);
break;
}
}
}
Scores = ScoreGeneration(MinMaxFlag, Agents);
for (int i = 0; i < PopulationSize; ++i)
{
if (double.IsNaN(Scores[i]))
{
StringBuilder sb = new StringBuilder("Error initialising differential evolution! (");
for (int j = 0; j < Dimensionality - 1; ++j)
{
sb.Append(Agents[i, j] + ",");
}
sb.Append(Agents[i, Dimensionality - 1] + ")");
throw new Exception(sb.ToString());
}
}
}
public void Initialise(int dimensionality, int populationSize, double differentialWeight, double crossoverProbability)
{
Dimensionality = dimensionality;
PopulationSize = populationSize;
DifferentialWeight = differentialWeight;
CrossoverProbability = crossoverProbability;
ParallelOptions = new ParallelOptions();
ParallelOptions.MaxDegreeOfParallelism = 1;
// It's not possible to run this in parallel because generations need to run sequentially in
// order to converge towards good solutions.
//ParallelOptions.MaxDegreeOfParallelism = Environment.ProcessorCount;
InitialisationRNG = new SobolRNG(Dimensionality, 777);
MutationRNG = new SobolRNG(4, 123);
RecombinationRNG = new SobolRNG(Dimensionality, 456);
PreviousParameterSets = new Dictionary <double[], bool>();
}
