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Erschienen in:
2009 | OriginalPaper | Buchkapitel
A Rigorous Evaluation of Crossover and Mutation in Genetic Programming
verfasst von : David R. White, Simon Poulding
Erschienen in: Genetic Programming
Verlag: Springer Berlin Heidelberg
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The role of crossover and mutation in Genetic Programming (GP) has been the subject of much debate since the emergence of the field. In this paper, we contribute new empirical evidence to this argument using a rigorous and principled experimental method applied to six problems common in the GP literature. The approach tunes the algorithm parameters to enable a fair and objective comparison of two different GP algorithms, the first using a combination of crossover and reproduction, and secondly using a combination of mutation and reproduction. We find that crossover does not significantly outperform mutation on most of the problems examined. In addition, we demonstrate that the use of a straightforward Design of Experiments methodology is effective at tuning GP algorithm parameters.