/// <summary>
/// Runs a random walk of n steps along a landscape defined by a crossover operator and returns its results.
/// </summary>
/// <param name="steps">Number of steps.</param>
/// <param name="searchOperator">Operator defining a neighbourhood.</param>
/// <returns></returns>
public RandomWalk RandomWalk(int steps, CrossoverOperator searchOperator)
{
RandomWalk statistics = new RandomWalk(steps);
Chromosome parent1 = ChromosomeFactory.RandomSolution(Problem.GeneCount(), Problem);
Chromosome parent2 = ChromosomeFactory.RandomSolution(Problem.GeneCount(), Problem);
bool firstParent = RandomGeneratorThreadSafe.NextBool();
Chromosome parent = (firstParent ? parent1 : parent2), child;
GatherData(parent, 0, statistics);
const int minPopSize = 8, maxPopSize = 15;
int popSize = RandomGeneratorThreadSafe.NextInt(minPopSize, maxPopSize);
Chromosome[] supportPopulation = new Chromosome[popSize];
for (int i = 0; i < popSize; ++i)
{
supportPopulation[i] = ChromosomeFactory.RandomSolution(Problem.GeneCount(), Problem);
}
IGene[] childGenes1, childGenes2;
for (int i = 0; i < steps; ++i)
{
searchOperator.Run(parent1.Genes, parent2.Genes, out childGenes1, out childGenes2);
child = ChromosomeFactory.MakeChromosome(Problem, RandomGeneratorThreadSafe.NextBool() ? childGenes1 : childGenes2);
GatherData(child, i, statistics);
parent1 = child;
parent2 = supportPopulation[RandomGeneratorThreadSafe.NextInt(popSize)];
}
return(statistics);
}
private bool[] PrepareUniform(int geneCount)
{
return(Enumerable.Repeat(0, geneCount).Select(i => RandomGeneratorThreadSafe.NextBool()).ToArray());
}
