// Does calculation of WCET.
private void calculation()
{
// Check if automatic or manual calculation.
if (_automatic) {
// Start loop and create new Generation.
Generation myGeneration = new Generation(_aSettings.populationSize, _param, _aSettings.mutationRate, _aSettings.crossoverCount, this);
_bestGenom = myGeneration.getBestGenom();
do
{
// Call Functions in GUI to show results.
_printResult(myGeneration, myGeneration.getBestGenom());
// Crossover and Mutate Generation.
myGeneration.crossover();
myGeneration.mutate();
// Select Genes from Generation with selected Selection Strategy & Create new Generation but use existing genoms.
myGeneration = new Generation(_aSettings.strategy.select(generation2Array(myGeneration)), _aSettings.populationSize, _aSettings.mutationRate, _aSettings.crossoverCount, this);
// Change bestGenom if necessary.
if (_bestGenom.fittness < myGeneration.getBestGenom().fittness) {
_bestGenom = myGeneration.getBestGenom();
}
} while (again());
// Finish and return Genom with WCET.
_finishedWCET(_bestGenom);
}
// Do Manual calculation.
else {
// Create Genom.
Genom myGenom = new Genom(_param,this);
// Return Genom to GUI.
_finishedManual(myGenom);
}
}
private void createGenes(uint givenGenes)
{
Parameter genomParameter;
Random ran;
int countAnalog = _blaram.analog.Length;
int countDigital = _blaram.digital.Length;
int countEnums = _blaram.enums.Length;
float[] analogVal = new float[countAnalog];
bool[] digitalVal = new bool[countDigital];
int[] enumVal = new int[countEnums];
for (uint k = givenGenes; k < this._size; k++)
{
ran = new Random();
for (int i = 0; i < countAnalog; i++) // Erzeugung Random Analogwerte
{
analogVal[i] = (float)ran.NextDouble();
}
for (int i = 0; i < countDigital; i++) // Erzeugung Random Digitalwerte
{
digitalVal[i] = rndBoolean();
}
for (int i = 0; i < countEnums; i++) // Erzeugung Random Enum werte
{
enumVal[i] = ran.Next(10);
}
genomParameter = new Parameter(analogVal, digitalVal, enumVal); //Parameter und Genomerzeugung
_genomArray[k] = new Genom(genomParameter,_ea);
//Calculate Fittness.
}
}
public Genom getBestGenom()
{
Genom dummy = new Genom(null,null);
dummy.fittness = 0;
for (int k = 0; k < _genomArray.Length; k++)
{
if (_genomArray[k].fittness > dummy.fittness)
{
dummy = _genomArray[k];
}
}
return dummy;
}
public Genom getBestGenom()
{
Genom dummy = new Genom(null);
return dummy;
}
