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r3048	r3053
25	25	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26	26
27		namespace HeuristicLab.Encodings.RealVector {
	27	namespace HeuristicLab.Encodings.RealVectorEncoding {
28	28	/// <summary>
29	29	/// The average crossover (intermediate recombination) calculates the average or centroid of a number of parent vectors.

trunk/sources/HeuristicLab.Encodings.RealVectorEncoding/3.3/Crossovers/BlendAlphaBetaCrossover.cs

r3048	r3053
26	26	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27	27
28		namespace HeuristicLab.Encodings.RealVector {
	28	namespace HeuristicLab.Encodings.RealVectorEncoding {
29	29	/// <summary>
30	30	/// Blend alpha-beta crossover for real vectors (BLX-a-b). Creates a new offspring by selecting a

r3048	r3053
26	26	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27	27
28		namespace HeuristicLab.Encodings.RealVector {
	28	namespace HeuristicLab.Encodings.RealVectorEncoding {
29	29	/// <summary>
30	30	/// Blend alpha crossover for real vectors (BLX-a). Creates a new offspring by selecting a random value

r3048	r3053
26	26	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27	27
28		namespace HeuristicLab.Encodings.RealVector {
	28	namespace HeuristicLab.Encodings.RealVectorEncoding {
29	29	/// <summary>
30	30	/// Discrete crossover for real vectors: For each position in the new vector an allele

r3048	r3053
26	26	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27	27
28		namespace HeuristicLab.Encodings.RealVector {
	28	namespace HeuristicLab.Encodings.RealVectorEncoding {
29	29	/// <summary>
30	30	/// Heuristic crossover for real vectors: Calculates the vector from the worse to the better parent and adds that to the better parent weighted with a factor in the interval [0;1).

r3048	r3053
25	25	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26	26
27		namespace HeuristicLab.Encodings.RealVector {
	27	namespace HeuristicLab.Encodings.RealVectorEncoding {
28	28	/// <summary>
29	29	/// The local crossover for real vectors is similar to the <see cref="UniformAllPositionsArithmeticCrossover"/>, but where the factor alpha is chosen randomly in the interval [0;1) for each position.

r3048	r3053
25	25	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26	26
27		namespace HeuristicLab.Encodings.RealVector {
	27	namespace HeuristicLab.Encodings.RealVectorEncoding {
28	28	/// <summary>
29	29	/// The random convex crossover is similar to the <see cref="LocalCrossover"/>, but chooses just one random alpha for all positions.

r3048	r3053
26	26	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27	27
28		namespace HeuristicLab.Encodings.RealVector {
	28	namespace HeuristicLab.Encodings.RealVectorEncoding {
29	29	/// <summary>
30	30	/// Performs the simulated binary crossover (SBX) on a vector of real values such that each position is either crossed contracted or expanded with a certain probability.

r3048	r3053
25	25	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26	26
27		namespace HeuristicLab.Encodings.RealVector {
	27	namespace HeuristicLab.Encodings.RealVectorEncoding {
28	28	/// <summary>
29	29	/// Single point crossover for real vectors. The implementation is similar to the single point crossover for binary vectors.

r3048	r3053
26	26	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27	27
28		namespace HeuristicLab.Encodings.RealVector {
	28	namespace HeuristicLab.Encodings.RealVectorEncoding {
29	29	/// <summary>
30	30	/// The uniform all positions arithmetic crossover constructs an offspring by calculating x = alpha * p1 + (1-alpha) * p2 for every position x in the vector.

r3048	r3053
26	26	using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27	27
28		namespace HeuristicLab.Encodings.RealVector {
	28	namespace HeuristicLab.Encodings.RealVectorEncoding {
29	29	/// <summary>
30	30	/// The uniform all positions arithmetic crossover (continuous recombination) constructs an offspring by calculating x = alpha * p1 + (1-alpha) * p2 for a position x in the vector with a given probability (otherwise p1 is taken at this position).

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