Crossover (evolutionary algorithm)

Crossover in evolutionary algorithms and evolutionary computation, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and is analogous to the crossover that happens during sexual reproduction in biology. New solutions can also be generated by cloning an existing solution, which is analogous to asexual reproduction. Newly generated solutions may be mutated before being added to the population. The aim of recombination is to transfer good characteristics from two different parents to one child.

Different algorithms in evolutionary computation may use different data structures to store genetic information, and each genetic representation can be recombined with different crossover operators. Typical data structures that can be recombined with crossover are bit arrays, vectors of real numbers, or trees.

The list of operators presented below is by no means complete and serves mainly as an exemplary illustration of this dyadic genetic operator type. More operators and more details can be found in the literature.^[1]^[2]^[3]^[4]^[5]

^ Davis, Lawrence (1991). Handbook of genetic algorithms. New York: Van Nostrand Reinhold. ISBN 0-442-00173-8. OCLC 23081440.
^ Eiben, A.E.; Smith, J.E. (2015). "Representation, Mutation, and Recombination". Introduction to Evolutionary Computing. Natural Computing Series (2nd ed.). Berlin, Heidelberg: Springer. pp. 49–78. doi:10.1007/978-3-662-44874-8. ISBN 978-3-662-44873-1. S2CID 20912932.
^ Yu, Xinjie; Gen, Mitsuo (2010). "Encoding and Operators". Introduction to evolutionary algorithms. Decision Engineering. London: Springer. pp. 40–63. doi:10.1007/978-1-84996-129-5. ISBN 978-1-84996-129-5. OCLC 654380156.
^ Yu, Xinjie; Gen, Mitsuo (2010). "Variation Operators for Permutation Code". Introduction to Evolutionary Algorithms. Decision Engineering. London: Springer. pp. 285–299. doi:10.1007/978-1-84996-129-5. ISBN 978-1-84996-128-8.
^ Booker, Lashon B.; Fogel, David B.; Whitley, Darrell; Angeline, Peter J.; Eiben, A.E. (2000). "Recombination". In Bäck, Thomas; Fogel, David B.; Michalewicz, Zbigniew (eds.). Evolutionary computation. Vol. 1, Basic algorithms and operators. Bristol: Institute of Physics Pub. pp. 256–307. ISBN 0-585-30560-9. OCLC 45730387.

[:0-1] Davis, Lawrence (1991). Handbook of genetic algorithms. New York: Van Nostrand Reinhold. ISBN 0-442-00173-8. OCLC 23081440.

[:1-2] Eiben, A.E.; Smith, J.E. (2015). "Representation, Mutation, and Recombination". Introduction to Evolutionary Computing. Natural Computing Series (2nd ed.). Berlin, Heidelberg: Springer. pp. 49–78. doi:10.1007/978-3-662-44874-8. ISBN 978-3-662-44873-1. S2CID 20912932.

[3] Yu, Xinjie; Gen, Mitsuo (2010). "Encoding and Operators". Introduction to evolutionary algorithms. Decision Engineering. London: Springer. pp. 40–63. doi:10.1007/978-1-84996-129-5. ISBN 978-1-84996-129-5. OCLC 654380156.

[4] Yu, Xinjie; Gen, Mitsuo (2010). "Variation Operators for Permutation Code". Introduction to Evolutionary Algorithms. Decision Engineering. London: Springer. pp. 285–299. doi:10.1007/978-1-84996-129-5. ISBN 978-1-84996-128-8.

[:5-5] Booker, Lashon B.; Fogel, David B.; Whitley, Darrell; Angeline, Peter J.; Eiben, A.E. (2000). "Recombination". In Bäck, Thomas; Fogel, David B.; Michalewicz, Zbigniew (eds.). Evolutionary computation. Vol. 1, Basic algorithms and operators. Bristol: Institute of Physics Pub. pp. 256–307. ISBN 0-585-30560-9. OCLC 45730387.

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Crossover (evolutionary algorithm)

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