Abstract
A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with offspring selection are still largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring (success rate) when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators does not improve the performance of the algorithm in terms of best solution quality or efficiency.
The work described in this paper was done within HEUREKA!, the Josef Ressel centre for heuristic optimization sponsored by the Austrian Research Promotion Agency (FFG).
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Kronberger, G., Winkler, S., Affenzeller, M., Wagner, S. (2009). On Crossover Success Rate in Genetic Programming with Offspring Selection. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds) Genetic Programming. EuroGP 2009. Lecture Notes in Computer Science, vol 5481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01181-8_20
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DOI: https://doi.org/10.1007/978-3-642-01181-8_20
Publisher Name: Springer, Berlin, Heidelberg
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