/// <summary>
/// Initializes a new instance of the class.
/// </summary>
/// <param name="randomSeed">The random seed.</param>
/// <param name="maximumIterations">The maximum number of iterations.</param>
/// <param name="maximumIterationsWithoutImprovement">The maximum number of iterations without improvement.</param>
/// <param name="maximumRunningTime">The maximum number of seconds.</param>
/// <param name="maximumPathLength">The maximum path length.</param>
/// <param name="populationSize">The population size.</param>
/// <param name="randomGenesPerChromosome">The maximum number of genes whose value can be simultaneously randomly generated.</param>
/// <param name="percentageRandom">The percentage of a population which is composed of randomly generated chromosomes.</param>
/// <param name="percentageElite">The percentage of a population which is composed of the elite chromosomes of the previous population.</param>
/// <param name="probabilityMutation">The probability of mutation for each gene of a chromosome.</param>
/// <param name="crossoverType">The crossover algorithm to be used.</param>
/// <param name="mutationType">The mutation algorithm to be used.</param>
public Parameters(int randomSeed, int maximumIterations, int maximumIterationsWithoutImprovement, int maximumRunningTime, int maximumPathLength, int populationSize, int randomGenesPerChromosome, double percentageRandom, double percentageElite, double probabilityMutation, AnalysisCrossoverType crossoverType, AnalysisMutationType mutationType)
{
// Assign the value for each parameter.
RandomSeed = randomSeed;
MaximumIterations = maximumIterations;
MaximumIterationsWithoutImprovement = maximumIterationsWithoutImprovement;
MaximumRunningTime = maximumRunningTime;
MaximumPathLength = maximumPathLength;
PopulationSize = populationSize;
RandomGenesPerChromosome = randomGenesPerChromosome;
PercentageRandom = percentageRandom;
PercentageElite = percentageElite;
ProbabilityMutation = probabilityMutation;
CrossoverType = crossoverType;
MutationType = mutationType;
}
/// <summary>
/// Creates a new offspring chromosome from this and a second chromosome parents.
/// </summary>
/// <param name="secondChromosome">The second parent chromosome of the offspring.</param>
/// <param name="nodeIndex">The dictionary containing, for each node, its index in the node list.</param>
/// <param name="powersMatrixCA">The list containing the different powers of the matrix (CA, CA^2, CA^3, ... ).</param>
/// <param name="nodeIsPreferred">The dictionary containing, for each node, if it is in the preferred node list.</param>
/// <param name="crossoverType">The crossover type for the algorithm.</param>
/// <param name="random">The random seed.</param>
/// <returns>A new offspring chromosome from this and a second chromosome parents.</returns>
public Chromosome Crossover(Chromosome secondChromosome, Dictionary <string, int> nodeIndex, List <Matrix <double> > powersMatrixCA, Dictionary <string, bool> nodeIsPreferred, AnalysisCrossoverType crossoverType, Random random)
{
// Define a new chromosome.
var chromosome = new Chromosome(Genes.Keys.ToList());
// Define the number of tries in which to try and find a valid chromosome.
var tries = _tries;
// Get the number of occurances of each gene in this chromosome and which genes of each are preferred.
var occurrences = GetUniqueInputNodes()
.Concat(secondChromosome.GetUniqueInputNodes())
.Distinct()
.ToDictionary(item => item, item => Genes.Count(item1 => item1.Value == item) + secondChromosome.Genes.Count(item1 => item1.Value == item));
// Use the specified crossover type.
switch (crossoverType)
{
// If we have a standard crossover.
case AnalysisCrossoverType.WeightedRandom:
// Repeat while the chromosome is not valid.
while (tries > 0)
{
// Decrease the number of tries.
tries--;
// Go over each of the target nodes.
foreach (var item in chromosome.Genes.Keys.ToList())
{
// Get the number of occurances in each chromosome.
var inFirst = occurrences[Genes[item]];
var inSecond = occurrences[secondChromosome.Genes[item]];
// Assign to the gene in the chromosome its corresponding random parent value with the a probability depending on the occurances.
chromosome.Genes[item] = random.NextDouble() < (double)inFirst / (double)(inFirst + inSecond) ? Genes[item] : secondChromosome.Genes[item];
}
// Check if the chromosome is valid.
if (chromosome.IsValid(nodeIndex, powersMatrixCA))
{
// Exit the loop.
break;
}
}
// End the switch statement.
break;
// If we have a standard crossover with preference.
case AnalysisCrossoverType.WeightedRandomPreferred:
// Repeat while the chromosome is not valid.
while (tries > 0)
{
// Decrease the number of tries.
tries--;
// Go over each of the target nodes.
foreach (var item in chromosome.Genes.Keys.ToList())
{
// Get the number of occurances in each chromosome.
var inFirst = occurrences[Genes[item]];
var inSecond = occurrences[secondChromosome.Genes[item]];
// Check if the gene in any of the chromosomes is a preferred node.
var isPreferredFirst = nodeIsPreferred[Genes[item]];
var isPreferredSecond = nodeIsPreferred[secondChromosome.Genes[item]];
// Check if the first corresponding gene is preferred, and the second one isn't.
if (isPreferredFirst && !isPreferredSecond)
{
// Choose one of the parent genes with a probability depending on their occurances, the preferred node being two times more likely to be selected.
chromosome.Genes[item] = random.NextDouble() < (double)inFirst * 2 / (double)(inFirst * 2 + inSecond) ? Genes[item] : secondChromosome.Genes[item];
}
// Check if the second corresponding gene is preferred, and the first one isn't.
else if (!isPreferredFirst && isPreferredSecond)
{
// Choose one of the parent genes with a probability depending on their occurances, the preferred node being two times more likely to be selected.
chromosome.Genes[item] = random.NextDouble() < (double)inSecond * 2 / (double)(inFirst + inSecond * 2) ? secondChromosome.Genes[item] : Genes[item];
}
// Otherwise they both have the same state.
else
{
// Choose one of the parent genes with a probability depending on their occurances.
chromosome.Genes[item] = random.NextDouble() < (double)inFirst / (double)(inFirst + inSecond) ? Genes[item] : secondChromosome.Genes[item];
}
}
// Check if the chromosome is valid.
if (chromosome.IsValid(nodeIndex, powersMatrixCA))
{
// Exit the loop.
break;
}
}
// End the switch statement.
break;
// If we have a default crossover.
case AnalysisCrossoverType.Dominant:
// Repeat while the chromosome is not valid.
while (tries > 0)
{
// Decrease the number of tries.
tries--;
// Go over each of the target nodes.
foreach (var item in chromosome.Genes.Keys.ToList())
{
// Get the number of occurances in each chromosome.
var inFirst = occurrences[Genes[item]];
var inSecond = occurrences[secondChromosome.Genes[item]];
// Assign to the gene in the chromosome its corresponding random parent value with the a probability depending on the occurances.
chromosome.Genes[item] = inSecond < inFirst ? Genes[item] : inFirst < inSecond ? secondChromosome.Genes[item] : random.NextDouble() < 0.5 ? Genes[item] : secondChromosome.Genes[item];
}
// Check if the chromosome is valid.
if (chromosome.IsValid(nodeIndex, powersMatrixCA))
{
// Exit the loop.
break;
}
}
// End the switch statement.
break;
// If we have a default crossover with preference.
case AnalysisCrossoverType.DominantPreferred:
// Repeat while the chromosome is not valid.
while (tries > 0)
{
// Decrease the number of tries.
tries--;
// Go over each of the target nodes.
foreach (var item in chromosome.Genes.Keys.ToList())
{
// Check if the gene in any of the chromosomes is a preferred node.
var isPreferredFirst = nodeIsPreferred[Genes[item]];
var isPreferredSecond = nodeIsPreferred[secondChromosome.Genes[item]];
// Check if the first corresponding gene is preferred, and the second one isn't.
if (isPreferredFirst && !isPreferredSecond)
{
// Choose the preferred node.
chromosome.Genes[item] = Genes[item];
}
// Check if the second corresponding gene is preferred, and the first one isn't.
else if (!isPreferredFirst && isPreferredSecond)
{
// Choose the preferred node.
chromosome.Genes[item] = secondChromosome.Genes[item];
}
// Otherwise they both have the same state.
else
{
// Get the number of occurances in each chromosome.
var inFirst = occurrences[Genes[item]];
var inSecond = occurrences[secondChromosome.Genes[item]];
// Choose one of the parent genes with a probability depending on their occurances.
chromosome.Genes[item] = inSecond < inFirst ? Genes[item] : inFirst < inSecond ? secondChromosome.Genes[item] : random.NextDouble() < 0.5 ? Genes[item] : secondChromosome.Genes[item];
}
}
// Check if the chromosome is valid.
if (chromosome.IsValid(nodeIndex, powersMatrixCA))
{
// Exit the loop.
break;
}
}
// End the switch statement.
break;
// If we have none of the above.
default:
// Set the tries to 0, such that the new chromosome will inherit completely one of the parents.
tries = 0;
// End the switch statement.
break;
}
// Check if the new chromosome is still not valid.
if (tries == 0)
{
// Choose randomly a parent to give all of its genes.
chromosome.Genes = random.NextDouble() < 0.5 ? new Dictionary <string, string>(Genes) : new Dictionary <string, string>(secondChromosome.Genes);
}
// Return the new chromosome.
return(chromosome);
}
