private static Tuple <int, double, double> SimulatedAnnealingExp(Fitness value, int count)
{
var historicalFitnesses = Enumerable.Range(1, count).Select(f => new Fitness(f)).Reverse().ToList();
var index = historicalFitnesses.BinarySearch(value);
if (index < 0)
{
index = ~index;
}
var difference = historicalFitnesses.Count - index;
var proportionSimilar = (double)difference / historicalFitnesses.Count;
var exp = Math.Exp(-proportionSimilar);
return(new Tuple <int, double, double>(index, proportionSimilar, exp));
}
private static void Generate(int diagonalSize, int?maxAge)
{
var watch = Stopwatch.StartNew();
var nSquared = diagonalSize * diagonalSize;
var geneSet = Enumerable.Range(1, nSquared).ToArray();
var expectedSum = diagonalSize * (nSquared + 1) / 2;
var geneIndexes = Enumerable.Range(0, geneSet.Length).ToArray();
Fitness FnGetFitness(IReadOnlyList <int> genes) => GetFitness(genes, diagonalSize, expectedSum);
void FnDisplay(Chromosome <int, Fitness> candidate) => Display(candidate, watch, diagonalSize);
void FnMutate(List <int> genes) => Mutate(genes, geneIndexes);
List <int> FnCreate() => geneSet.OrderBy(i => Rand.Random.Next(geneSet.Length)).ToList();
var optimalValue = new Fitness(0);
var best = Genetic <int, Fitness> .GetBest(FnGetFitness, nSquared, optimalValue, geneSet, FnDisplay, FnMutate,
FnCreate, maxAge);
Assert.IsTrue(optimalValue.CompareTo(best.Fitness) <= 0);
}
