A rigorous evaluation of crossover and mutation in genetic programming

White, D.R. and Poulding, S. (2009) A rigorous evaluation of crossover and mutation in genetic programming. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I. and Ebner, M. (eds.) 12th European Conference, EuroGP 2009 Tübingen, Germany, April 15-17, 2009 Proceedings. Series: Lecture Notes in Computer Science, 5481. Springer Verlag: Germany, pp. 220-231. (doi: 10.1007/978-3-642-01181-8_19)

Abstract

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.

• Item Type: Book Sections
• Additional Information: The original publication is available at www.springerlink.com
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: White, Dr David
• Authors: White, D.R., and Poulding, S.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer Verlag
• Copyright Holders: Copyright © 2009 Springer
• First Published: First published in Lecture Notes in Computer Science 5481:220-231
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher