public Algorithm(int generation, double probability, AlgorithmType type, IParentSelection parentSelection)
{
numberOfGenerations = generation;
probabilityOfMutation = probability;
typeOfAlgorithm = type;
this.parentSelection = parentSelection;
}
public Generation(List <Individual> population, double probability, AlgorithmType type, IParentSelection parentSelection)
{
startingPopulation = population;
probabilityOfMutation = probability;
typeOfAlgorithm = type;
this.parentSelection = parentSelection;
}
public Generation(Individual[] currentPopulation, Cities cities, IParentSelection parentSelection, ICrossover crossoverOperator, IMutation methodOfMutation)
{
this.population = currentPopulation;
this.cities = cities;
this.parentSelection = parentSelection;
this.crossoverOperator = crossoverOperator;
this.methodOfMutation = methodOfMutation;
}
public Algorithm(int generation, int execution, double probability, AlgorithmType type, IParentSelection parentSelection)
{
numberOfGenerations = generation;
numberOfExecution = execution;
probabilityOfMutation = probability;
typeOfAlgorithm = type;
arrayOfBestResults = new double[execution];
this.parentSelection = parentSelection;
}
public TSPAlgorithm(int numberOfGenerations, int populationSize, Cities cities,
IParentSelection parentSelection, IMutation methodOfMutation, ICrossover crossoverOperator)
{
this.numberOfGenerations = numberOfGenerations;
this.populationSize = populationSize;
this.cities = cities;
this.parentSelection = parentSelection;
this.crossoverOperator = crossoverOperator;
this.methodOfMutation = methodOfMutation;
this.bestResults = new double[numberOfGenerations];
}
public SolutionStrategyFactory(
IRandomSolutionGenerator randomSolutionGenerator,
IParentSelection parentSelection,
IRecombination recombination,
IBestSolutionFinder bestSolutionFinder,
IMutation mutation,
ISolutionEvaluator solutionEvaluator,
IAdptiveChanceAdjuster adptiveChanceAdjuster,
ICSVFileWriter csvFileWriter
)
{
this.randomSolutionGenerator = randomSolutionGenerator;
this.parentSelection = parentSelection;
this.recombination = recombination;
this.bestSolutionFinder = bestSolutionFinder;
this.mutation = mutation;
this.solutionEvaluator = solutionEvaluator;
this.adptiveChanceAdjuster = adptiveChanceAdjuster;
this.csvFileWriter = csvFileWriter;
}
public EvolutionarySolution(
IRandomSolutionGenerator randomSolutionGenerator,
IParentSelection parentSelection,
IRecombination recombination,
IBestSolutionFinder bestSolutionFinder,
IMutation mutation,
ISolutionEvaluator solutionEvaluator,
IAdptiveChanceAdjuster adptiveChanceAdjuster
)
{
this.randomSolutionGenerator = randomSolutionGenerator;
this.parentSelection = parentSelection;
this.recombination = recombination;
this.bestSolutionFinder = bestSolutionFinder;
this.mutation = mutation;
this.solutionEvaluator = solutionEvaluator;
this.adptiveChanceAdjuster = adptiveChanceAdjuster;
solutions = new Dictionary <int, Solution>();
parentPopulation = new List <Solution>();
offspringPopulation = new List <Solution>();
}
public void SetUp()
{
// This can't be good practice. is there a way to set a constant value both locally and temporarily?
EvolutionaryAlgorithmConstants.TOURNAMENT_SIZE = 5;
parentSelection = new ParentSelection();
}
