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