private void Solve(int iteration, ref CMAState cmaState, ref Solution solution, bool isOnce = false)
{
//ColumnVector xmean = new ColumnVector(parameters);
double tolX = this.StandardDeviationToleranceMultiple * this.InitialVarianceEstimate;
int stopeval = this.StopEvals;
RectangularMatrix arx = new RectangularMatrix(cmaState.p.N, cmaState.p.Lambda);
ColumnVector arfitness = new ColumnVector(cmaState.p.Lambda);
bool isRestart = false;
bool isGoodEnough = false;
int counteval = 0;
while (counteval < stopeval)
{
// Generate and evaluate lambda offspring
for (int k = 0; k < cmaState.p.Lambda; k++)
{
double[] sample = cmaState.Sample(arx.Column(k));
for (int r = 0; r < cmaState.p.N; r++)
{
arx[r, k] = sample[r];
}
arfitness[k] = this.ObjectiveFunction(arx.Column(k).ToArray(), this.UserData);
counteval++;
}
// Sort by fitness and compute weighted mean into xmean
// minimization
int[] arindex;
arfitness = Utilities.Sort(arfitness, out arindex); // one based index
int mu = cmaState.p.Mu;
cmaState.ReEstimate(
(RectangularMatrix)Utilities.GetColumns(arx, arindex.Take(cmaState.p.Mu)),
arfitness.First(),
arfitness.SkipWhile((f, i) => i < (mu - 1)).First());
cmaState.UpdateEigenSystem(1, 0);
if ((solution.StopReason == StopReason.NotSet) || (arfitness[0] < solution.MinimizedFitness))
{
solution.StopReason = StopReason.Incomplete;
solution.MinimizedFitness = arfitness[0];
solution.BestCMAState = cmaState;
solution.BestNumEvals = counteval;
solution.BestNumRestarts = iteration;
solution.BestParameters = cmaState.mean.ToArray();
}
if (arfitness[0] <= this.StopFitnessMinimization)
{
isGoodEnough = true;
break;
}
if (IsRestart(ref solution, arfitness, tolX, cmaState))
{
isRestart = true;
break;
}
if (isOnce)
{
break;
}
}
if ((isRestart) && (iteration > this.MaxRestarts))
{
solution.StopReason = StopReason.MaxRestarts;
}
else if ((isRestart) && ((cmaState.p.Lambda * this.RestartPopulationMultiple) > MAX_POPULATIONSIZE))
{
solution.StopReason = StopReason.MaxPopulation;
}
else if (isRestart)
{
solution.History = new Queue <double>();
solution.StopReason = StopReason.Restart;
int previousLambda = cmaState.p.Lambda;
cmaState = new CMAState(_initialCmaParams, new ColumnVector(this.InitialParameters), this.InitialVarianceEstimate);
cmaState.p.UpdateLambda((int)(previousLambda * this.RestartPopulationMultiple));
}
else if (isGoodEnough)
{
solution.StopReason = StopReason.GoodEnough;
}
else
{
solution.StopReason = StopReason.MaxIterations;
}
solution.LatestCMAState = cmaState;
solution.LatestNumEvals = counteval;
solution.LatestNumRestarts = iteration;
solution.LatestParameters = cmaState.mean.ToArray();
}
