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Does Memetic Approach Improve Global Induction of Regression and Model Trees?

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7269)

Abstract

Memetic algorithms are popular approaches to improve pure evolutionary methods. But were and when in the system the local search should be applied and does it really speed up evolutionary search is a still an open question. In this paper we investigate the influence of the memetic extensions on globally induced regression and model trees. These evolutionary induced trees in contrast to the typical top-down approaches globally search for the best tree structure, tests at internal nodes and models at the leaves. Specialized genetic operators together with local greedy search extensions allow to the efficient tree evolution. Fitness function is based on the Bayesian information criterion and mitigate the over-fitting problem. The proposed method is experimentally validated on synthetical and real-life datasets and preliminary results show that to some extent memetic approach successfully improve evolutionary induction.

Keywords

  • data mining
  • evolutionary algorithms
  • memetic algorithms
  • regression trees
  • model trees
  • global induction

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Czajkowski, M., Kretowski, M. (2012). Does Memetic Approach Improve Global Induction of Regression and Model Trees?. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_20

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  • DOI: https://doi.org/10.1007/978-3-642-29353-5_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29352-8

  • Online ISBN: 978-3-642-29353-5

  • eBook Packages: Computer ScienceComputer Science (R0)