public RealRandomSearch(IEvaluation <double, double> evaluation, IStopCondition stopCondition, int?seed = null)
{
Result = null;
this.evaluation = evaluation;
this.stopCondition = stopCondition;
generator = new RealRandomGenerator(evaluation.pcConstraint, seed);
}
public ASimpleOptimizer(IEvaluation <Element> evaluation, AStopCondition stopCondition, AGenerator <Element> generator)
{
Result = null;
this.evaluation = evaluation;
this.stopCondition = stopCondition;
this.generator = generator;
}
public BinaryRandomSearch(IEvaluation <bool> evaluation, AStopCondition stopCondition, int?seed = null)
{
Result = null;
this.evaluation = evaluation;
this.stopCondition = stopCondition;
generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
}
public BinaryGreedyRSOptimizer(IEvaluation <bool> evaluation, AStopCondition stopCondition,
AStopCondition greedyStopCondition, int?seed = null)
: base(evaluation, stopCondition)
{
generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
greedyOptimizer = new BinaryGreedyOptimizer(evaluation, null, greedyStopCondition, seed);
}
public GeneticAlgorithm(IEvaluation <Element, Tuple <double, double> > evaluation, IStopCondition stopCondition,
AGenerator <Element> generator, ASelection <Tuple <double, double> > selection, ACrossover crossover,
IMutation <Element> mutation, int populationSize, int?seed = null)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize,
new OptimizationState <Element>(evaluation.tMaxValue, evaluation.lOptimalParetoFront), seed)
{
}
public ClusteringGeneticAlgorithm(IEvaluation <Element, Tuple <double, double> > evaluation, IStopCondition stopCondition,
AGenerator <Element> generator, ASelection <Tuple <double, double> > selection, ACrossover crossover,
IMutation <Element> mutation, int populationSize, int clusters, int?seed = null)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize, seed)
{
no_clusters = clusters;
rng = seed.HasValue ? new Random(seed.Value) : new Random();
}
public DSMGA(IEvaluation <bool> evaluation, AStopCondition stopCondition, AGenerator <bool> generator,
ASelection selection, ACrossover crossover, IMutation <bool> mutation, int populationSize)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize)
{
Combinations = GetKCombs(Enumerable.Range(0, evaluation.iSize), 2)
.Select(x => x.ToList()).ToList();
ResetContingencyMatrixTable();
}
public APopulationOptimizer(IEvaluation <Element> evaluation, AStopCondition stopCondition, AGenerator <Element> generator,
ASelection selection, IMutation <Element> mutation, int populationSize)
: base(evaluation, stopCondition)
{
this.generator = generator;
this.selection = selection;
this.mutation = mutation;
this.populationSize = populationSize;
population = new List <Individual <Element> >();
}
public EncodedGA(IEvaluation <Element> evaluation, AStopCondition stopCondition, AGenerator <Element> generator,
ASelection selection, ACrossover crossover, IMutation <Element> mutation, int populationSize, int?seed)
: base(evaluation, stopCondition)
{
this.generator = generator;
this.selection = selection;
this.mutation = mutation;
this.populationSize = populationSize;
population = new List <EncodedIndividual <Element> >();
this.seed = seed;
this.crossover = crossover;
}
public GeneticAlgorithm(IEvaluation <Element> evaluation, AStopCondition stopCondition, AGenerator <Element> generator,
ASelection selection, ACrossover crossover, IMutation <Element> mutation, int populationSize)
: base(evaluation, stopCondition, generator, selection, mutation, populationSize)
{
this.crossover = crossover;
}
protected virtual Node_Generator parseGenerator(AGenerator node)
{
return new Node_Generator(
parseOne<PExpression,INode_Expression>(parseExpression, (PExpression)node.GetType()),
parseOne<PExpression,INode_Expression>(parseExpression, (PExpression)node.GetBody()),
getSource(node));
}
public BaldwinGeneticAlgorithm(CBinaryKnapsackEvaluation evaluation, AStopCondition stopCondition, AGenerator <bool> generator,
ASelection selection, ACrossover crossover, IMutation <bool> mutation, int populationSize)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize)
{
}
public GeneticAlgorithm(IEvaluation <Element, EvaluationResult> evaluation, IStopCondition stopCondition, AGenerator <Element> generator,
ASelection <EvaluationResult> selection, ACrossover crossover, IMutation <Element> mutation, int populationSize,
AOptimizationState <Element, EvaluationResult, OptimizationResult> state, int?seed = null)
: base(evaluation, stopCondition, generator, selection, mutation, populationSize, state)
{
this.crossover = crossover;
indices = Utils.CreateIndexList(populationSize);
shuffler = new Shuffler(seed);
}
public IslandModel(IEvaluation <bool> evaluation, AStopCondition stopCondition, AGenerator <bool> generator,
ASelection selection, ACrossover crossover, IMutation <bool> mutation,
int globalPopulationSize, int islandCount, int maxNoChangeDetections,
APopulationCrossover <bool> populationCrossover)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, 0)
{
this.islandCount = islandCount;
this.globalPopulationSize = globalPopulationSize;
maxNoChangeDetections = maxNoChangeDetections;
this.populationCrossover = populationCrossover;
if (globalPopulationSize % islandCount != 0)
{
throw new Exception("liczba wysp jest nie podzielna przez wielkosc calej populacji");
}
}
public KnapsackGA(CBinaryKnapsackEvaluation evaluation, AStopCondition stopCondition, AGenerator <bool> generator,
ASelection selection, ACrossover crossover, IMutation <bool> mutation, int populationSize, EffectType effType, int?seed)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize)
{
this.seed = seed;
effectType = effType;
}
public NSGA2(IEvaluation <Element, Tuple <double, double> > evaluation, IStopCondition stopCondition, AGenerator <Element> generator,
IDominationComparer dominationComparer, ACrossover crossover, IMutation <Element> mutation, int populationSize,
int?seed = null)
: base(evaluation, stopCondition, generator, new NSGA2Selection(2, evaluation.tMaxValue, dominationComparer, true, seed),
crossover, mutation, populationSize, seed)
{
}
public GAWithDSM(IEvaluation <bool> evaluation, AStopCondition stopCondition, AGenerator <bool> generator,
ASelection selection, IMutation <bool> mutation, int populationSize,
double crossProb, double threshold)
: base(evaluation, stopCondition, generator, selection, new DSMCrossover(crossProb, threshold, null), mutation, populationSize)
{
}
public SamplingOptimizer(IEvaluation <Element> evaluation, AStopCondition stopCondition, AGenerator <Element> generator)
: base(evaluation, stopCondition)
{
this.generator = generator;
}
public GAwithDSM(IEvaluation <bool> evaluation, AStopCondition stopCondition, AGenerator <bool> generator,
ASelection selection, ACrossover crossover, IMutation <bool> mutation, int populationSize, int interval = 10)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize)
{
this.interval = interval;
dsms = new List <List <List <double> > >();
if (crossover is DSMCrossover)
{
List <List <double> > tempDSM = new List <List <double> >();
for (int i = 0; i < evaluation.iSize; i++)
{
tempDSM.Add(Enumerable.Repeat(0.0, evaluation.iSize).ToList());
}
((DSMCrossover)crossover).dsm = tempDSM;
}
}
public GAStallDetection(IEvaluation<Element> evaluation, AStopCondition stopCondition, AGenerator<Element> generator,
ASelection selection, ACrossover crossover, IMutation<Element> mutation, int populationSize,
int maxNoChangeDetections)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize)
{
MaxNCD = maxNoChangeDetections;
}
public MutatedIslandGA(IEvaluation <bool> evaluation, AStopCondition stopCondition, AGenerator <bool> generator,
ASelection selection, ACrossover crossover, IMutation <bool> mutation, int populationSize)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize)
{
var moreMutation = new BinaryBitFlipMutation(1, evaluation);
int mutatedPop = populationSize / 10 % 2 == 1 ? populationSize / 10 - 1 : populationSize / 10;
mutatedPop = mutatedPop == 0 ? 2 : mutatedPop;
mutantIsland = new GeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, moreMutation, mutatedPop);
mutantIsland.Initialize();
}
public KnapsackGeneticAlgorithm(CBinaryKnapsackEvaluation evaluation, AStopCondition stopCondition, AGenerator <bool> generator,
ASelection selection, ACrossover crossover, IMutation <bool> mutation, int populationSize)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize)
{
var emptySolution = Enumerable.Repeat(false, evaluation.iSize).ToList();
CheckNewBest(emptySolution, 0.0, true);
}
public SamplingOptimizer(IEvaluation <Element, double> evaluation, IStopCondition stopCondition, AGenerator <Element> generator)
: base(evaluation, stopCondition, new OptimizationState <Element>(evaluation.tMaxValue))
{
this.generator = generator;
}
public ResettingGA(IEvaluation <Element> evaluation, AStopCondition stopCondition, AGenerator <Element> generator,
ASelection selection, ACrossover crossover, IMutation <Element> mutation, int populationSize, int patience, int?seed = null)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize)
{
this.patience = patience;
rng = seed.HasValue ? new Random(seed.Value) : new Random();
}
