On Crossover Success Rate in Genetic Programming with Offspring Selection

Authors:
Gabriel Kronberger;Stephan Winkler;Michael Affenzeller;Stefan Wagner
Affiliations:
Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media - Hagenberg, Upper Austria University of Applied Sciences, Hagenberg, Austria A-4232;Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media - Hagenberg, Upper Austria University of Applied Sciences, Hagenberg, Austria A-4232;Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media - Hagenberg, Upper Austria University of Applied Sciences, Hagenberg, Austria A-4232;Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media - Hagenberg, Upper Austria University of Applied Sciences, Hagenberg, Austria A-4232
Venue:
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Year:
2009

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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.