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Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search

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EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 227))

Abstract

Bloat is one of the most interesting theoretical problems in genetic programming (GP), and one of the most important pragmatic limitations in the development of real-world GP solutions. Over the years, many theories regarding the causes of bloat have been proposed and a variety of bloat control methods have been developed. It seems that one of the underlying causes of bloat is the search for fitness; as the fitness-causes-bloat theory states, selective bias towards fitness seems to unavoidably lead the search towards programs with a large size. Intuitively, however, abandoning fitness does not appear to be an option. This paper, studies a GP system that does not require an explicit fitness function, instead it relies on behavior-based search, where programs are described by the behavior they exhibit and selective pressure is biased towards unique behaviors using the novelty search algorithm. Initial results are encouraging, the average program size of the evolving population does not increase with novelty search; i.e., bloat is avoided by focusing on novelty instead of quality.

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Authors and Affiliations

  1. 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

    Leonardo Trujillo, Enrique Naredo & Yuliana Martínez

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  1. Leonardo Trujillo
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  2. Enrique Naredo
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  3. Yuliana Martínez
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Correspondence to Leonardo Trujillo .

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Editors and Affiliations

  1. , Leiden Institute of Advanced, Leiden University, Niels Bohrweg 1, Leiden, 2333CA, Netherlands

    Michael Emmerich

  2. , Leiden Institute of Advanced, Leiden University, Niels Bohrweg 1, Leiden, 2333CA, Netherlands

    Andre Deutz

  3. , Depto. de Computacion, CINVESTAV-IPN, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico, 07360, Mexico

    Oliver Schuetze

  4. , Leiden Institute of Advanced, Leiden University, Niels Bohrweg 1, Leiden, 2333CA, Netherlands

    Thomas Bäck

  5. , Luxembourg Centre for, University of Luxembourg, Avenue des Hauts Fourneaux 7, Belval, 4362, Luxembourg

    Emilia Tantar

  6. , Computer Science and, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Alexandru-Adrian Tantar

  7. , Bordeaux Mathematical Institute, Université Bordeaux I, cours de la Libération 351, Talence cedex, 33405, France

    Pierre Del Moral

  8. , UFR Sciences et Modélisation, Université Bordeaux Segalen, 3ter place de la Victoire, Bordeaux, 33076, France

    Pierrick Legrand

  9. Faculty of Sci., Tech. & Communication, Computer Science and, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Pascal Bouvry

  10. , Depto. de Computatción, CINVESTAV-IPN, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico, 07360, Mexico

    Carlos A. Coello

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Trujillo, L., Naredo, E., Martínez, Y. (2013). Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search. In: Emmerich, M., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. Advances in Intelligent Systems and Computing, vol 227. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01128-8_19

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  • DOI: https://doi.org/10.1007/978-3-319-01128-8_19

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-01127-1

  • Online ISBN: 978-3-319-01128-8

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