/// <summary>
/// Evaluate the provided black box against the function regression task,
/// and return its fitness score.
/// </summary>
/// <param name="box">The black box to evaluate.</param>
/// <returns>A new instance of <see cref="FitnessInfo"/>.</returns>
public FitnessInfo Evaluate(IBlackBox <double> box)
{
// Probe the black box over the full range of the input parameter.
_blackBoxProbe.Probe(box, _yArr);
// Calc gradients.
FuncRegressionUtils.CalcGradients(_paramSamplingInfo, _yArr, _gradientArr);
// Calc y position mean squared error (MSE), and apply weighting.
double yMse = MathSpanUtils.MeanSquaredDelta(_yArr, _yArrTarget);
yMse *= _yMseWeight;
// Calc gradient mean squared error.
double gradientMse = MathSpanUtils.MeanSquaredDelta(_gradientArr, _gradientArrTarget);
gradientMse *= _gradientMseWeight;
// Calc fitness as the inverse of MSE (higher value is fitter).
// Add a constant to avoid divide by zero, and to constrain the fitness range between bad and good solutions;
// this allows the selection strategy to select solutions that are mediocre and therefore helps preserve diversity.
double fitness = 20.0 / (yMse + gradientMse + 0.02);
return(new FitnessInfo(fitness));
}
private static void MeanSquaredDelta_Inner(UniformDistributionSampler sampler, int len)
{
// Alloc arrays and fill with uniform random noise.
double[] a = new double[len];
double[] b = new double[len];
sampler.Sample(a);
sampler.Sample(b);
// Calc results and compare.
double expected = PointwiseSumSquaredDelta(a, b) / a.Length;
double actual = MathSpanUtils.MeanSquaredDelta(a, b);
Assert.Equal(expected, actual, 10);
}
