public void Test_IRRC()
{
const int cols = 20;
const int rows = 15;
var me = new IncreasingRowsRandomColumns(rows, cols);
var m = new bool[rows, cols];
for (int r = 0; r < rows; r++)
{
for (int c = 0; c < cols; c++)
{
m[r, c] = false;
}
}
int row, col;
int k = 0;
while (me.GetNext(out row, out col))
{
Assert.IsFalse(m[row, col]);
Assert.AreEqual(k++ / cols, row);
m[row, col] = true;
}
Assert.AreEqual(cols * rows, k);
foreach (var b in m)
{
Assert.IsTrue(b);
}
}
public void Test_IRRC_ZeroMatrix()
{
const int cols = 0;
const int rows = 0;
var me = new IncreasingRowsRandomColumns(rows, cols);
int r, c;
Assert.IsFalse(me.GetNext(out r, out c));
}
/// <summary>
/// Creates a population and let it evolve until termination condition is reached.
/// </summary>
/// <param name="terminationCondition">Evolution termination condition evaluated on each new epoch</param>
/// <returns>The best solution found across the evolution</returns>
public ISolution EvolveUntil(TerminationCondition terminationCondition)
{
var rnd = RandomProvider.GetThreadRandom();
var shiftMatrix = MatrixBuilder.Build(this.problem);
var enumerator = new IncreasingRowsRandomColumns(this.problem.Days, this.problem.Slots);
var constraints = ConstraintsBuilder.Build(this.problem);
var chromoProcessor = new ChromosomeProcessor(shiftMatrix, enumerator, constraints);
var evaluator = new FitnessEvaluator(problem);
var fitnessFunction = new Fitness.FitnessFunction(chromoProcessor, evaluator, shiftMatrix);
var chromosomeLength = shiftMatrix.GetNumberOfUnforcedSlots();
const double crossoverProbability = 0.90;
const double mutationProbability = 0.05;
const int elitismPercentage = 5;
epochs = 1;
epochsWithoutFitnessImprovement = 0;
overallBestFitness = -1;
log.Debug("Starting population.");
var population = new Population();
for (var i = 0; i < populationSize; i++)
{
var c = new Double[chromosomeLength];
for (var k = 0; k < chromosomeLength; k++)
{
c[k] = rnd.NextDouble();
}
var ch = new Chromosome(c);
population.Solutions.Add(ch);
}
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new SwapMutate(mutationProbability);
//create the GA itself
var ga = new GeneticAlgorithm(population, fitnessFunction.Evaluate);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += Ga_OnGenerationComplete;
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run((pop, currentGeneration, currentEvaluation) =>
{
return(terminationCondition(currentGeneration, epochsWithoutFitnessImprovement, population.MaximumFitness, population.AverageFitness));
});
population.GetTop(1)[0].Evaluate(fitnessFunction.Evaluate);
return(SolutionBuilder.Build(overallBestFitness, shiftMatrix, epochs * population.PopulationSize, this.problem.Items));
}
