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EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V pp 201–212Cite as

A Comparison of Fitness-Case Sampling Methods for Symbolic Regression with Genetic Programming

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 288))

Abstract

The canonical approach towards fitness evaluation in Genetic Programming (GP) is to use a static training set to determine fitness, based on a cost function averaged over all fitness-cases. However, motivated by different goals, researchers have recently proposed several techniques that focus selective pressure on a subset of fitness-cases at each generation. These approaches can be described as fitness-case sampling techniques, where the training set is sampled, in some way, to determine fitness. This paper shows a comprehensive evaluation of some of the most recent sampling methods, using benchmark and real-world problems for symbolic regression. The algorithms considered here are Interleaved Sampling, Random Interleaved Sampling, Lexicase Selection and a new sampling technique is proposed called Keep-Worst Interleaved Sampling (KW-IS). The algorithms are extensively evaluated based on test performance, overfitting and bloat. Results suggest that sampling techniques can improve performance compared with standard GP. While on synthetic benchmarks the difference is slight or none at all, on real-world problems the differences are substantial. Some of the best results were achieved by Lexicase Selection and Keep Worse-Interleaved Sampling. Results also show that on real-world problems overfitting correlates strongly with bloating. Furthermore, the sampling techniques provide efficiency, since they reduce the number of fitness-case evaluations required over an entire run.

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Authors and Affiliations

  1. TREE-LAB, Doctorado en Ciencias de la Ingeniería, Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Tijuana, Blvd. Industrial y Av. ITR Tijuana S/N, Mesa Otay, C.P. 22500, Tijuana, B.C., México

    Yuliana Martínez, Leonardo Trujillo & Enrique Naredo

  2. UMR CNRS 5251, Université Victor Segalen Bordeaux 2 and The Institut de Mathmatiques de Bordeaux, Bordeaux, France

    Pierrick Legrand

  3. ALEA Team, INRIA Bordeaux Sud-Ouest, Talence, France

    Pierrick Legrand

Authors
  1. Yuliana Martínez
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  2. Leonardo Trujillo
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  3. Enrique Naredo
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  4. Pierrick Legrand
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Corresponding author

Correspondence to Yuliana Martínez .

Editor information

Editors and Affiliations

  1. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg

    Alexandru-Adrian Tantar

  2. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg

    Emilia Tantar

  3. School of Engineering, University of California, Merced, California, USA

    Jian-Qiao Sun

  4. College of Mechanical Engineering, Beijing University of Technology, Beijing, China

    Wei Zhang

  5. Department of Mechanics, Tianjin University, Tianjin, China

    Qian Ding

  6. Depto. Computación, CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  7. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Michael Emmerich

  8. Université de Bordeaux, Bordeaux, France

    Pierrick Legrand

  9. School of Mathematics and Statistics, University of New South Wales, Sydney, New South Wales, Australia

    Pierre Del Moral

  10. Computer Science Department, CINVESTAV-IPN, Mexico City, Mexico

    Carlos A. Coello Coello

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© 2014 Springer International Publishing Switzerland

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Martínez, Y., Trujillo, L., Naredo, E., Legrand, P. (2014). A Comparison of Fitness-Case Sampling Methods for Symbolic Regression with Genetic Programming. In: Tantar, AA., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. Advances in Intelligent Systems and Computing, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-319-07494-8_14

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  • DOI: https://doi.org/10.1007/978-3-319-07494-8_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07493-1

  • Online ISBN: 978-3-319-07494-8

  • eBook Packages: EngineeringEngineering (R0)

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