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= Crossover = 
Crossovers are HeuristicLab 3.3 operators that implement the `ICrossover` interface. Crossover operators for different encodings have already been implemented, such as: 

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== Crossover for !BinaryVectorEncoding == 
=== !MultiBinaryVectorCrossover ===
Randomly selects and applies one of its crossovers every time it is called.


'''Operator Parameters:'''

||= Parameter         =||= Description =||

=== NPointCrossover ===
N point crossover for binary vectors. It is implemented as described in (Eiben and Smith 2003).

=== !MultiBinaryVectorCrossover ===
Randomly selects and applies one of its crossovers every time it is called.

=== !SinglePointCrossover ===
Single point crossover for binary vectors. It is implemented based on the NPointCrossover.

=== !UniformCrossover ===
Uniform crossover for binary vectors. It is implemented as described in (Eiben and Smith 2003).

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== Crossover for !IntegerVectorEncoding == 
=== !DiscreteCrossover ===
Discrete crossover for integer vectors. It is implemented as described in (Gwiazda 2006, p. 17).

=== !MultiIntegerVectorCrossover ===
Randomly selects and applies one of its crossovers every time it is called.

=== !SinglePointCrossover ===
Single point crossover for integer vectors. It is implemented as described in (Michalewicz 1999).
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== Crossover for !PermutationEncoding == 
=== !CosaCrossover ===
An operator which performs the crossover described in the COSA optimization method. It is implemented as described in (Wendt 1994). 

=== !CyclicCrossover ===
An operator which performs the cyclic crossover on two permutations. It is implemented as described in (Eiben and Smith 2003).

=== !CyclicCrossover2 ===
An operator which performs the cyclic crossover on two permutations. It is implemented as described in (Affenzeller et al. 2009, p. 136)

=== !EdgeRecombinationCrossover ===
An operator which performs the edge recombination crossover on two permutations. It is implemented as described in (Whitley et al. 1991).

== !MaximalPreservativeCrossover ==
An operator which performs the maximal preservative crossover on two permutations. It is implemented as described in (Mühlenbein 1991). 

=== !MultiPermutationCrossover === 
Randomly selects and applies one of its crossovers every time it is called.

=== !OrderBasedCrossover ===
An operator which performs an order based crossover of two permutations. It is implemented as described in (Syswerda 1991).

=== !OrderCrossover === 
An operator which performs an order crossover of two permutations. It is implemented as described in (Eiben and Smith 2003).

=== !OrderCrossover2 ===
An operator which performs an order crossover of two permutations. It is implemented as described in (Affenzeller et al. 2009, p. 135).

=== !PartiallyMatchedCrossover ===
An operator which performs the partially matched crossover on two permutations. It is implemented as described in (Fogel 1988). 

=== !PositionBasedCrossover === 
An operator which performs the position based crossover on two permutations. It is implemented as described in (Syswerda 1991). 

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== References == 
 * Affenzeller, M. et al. 2009. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. CRC Press.
 * Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg.
 * Fogel, D.B. 1988. An Evolutionary Approach to the Traveling Salesman Problem. Biological Cybernetics, 60, pp. 139-144, Springer-Verlag.
 * Gwiazda, T.D. 2006. Genetic algorithms reference Volume I Crossover for single-objective numerical optimization problems.
 * Michalewicz, Z. 1999. Genetic Algorithms + Data Structures = Evolution Programs. Third, Revised and Extended Edition, Springer-Verlag Berlin Heidelberg.
 * Mühlenbein, H. 1991. Evolution in time and space - the parallel genetic algorithm. FOUNDATIONS OF GENETIC ALGORITHMS. Morgan Kaufmann. pp. 316-337.
 * Syswerda, G. 1991. Schedule Optimization Using Genetic Algorithms. In Davis, L. (Ed.) Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, pp. 332-349.
 * Wendt, O. 1994. COSA: COoperative Simulated Annealing - Integration von Genetischen Algorithmen und Simulated Annealing am Beispiel der Tourenplanung. Dissertation Thesis. IWI Frankfurt.
 * Whitley et.al. 1991, The Traveling Salesman and Sequence Scheduling, in Davis, L. (Ed.), Handbook of Genetic Algorithms, New York. pp. 350-372