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Brazilian Symposium on Artificial Intelligence

SBIA 2002: Advances in Artificial Intelligence pp 345–354Cite as

Controlling the Population Size in Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2507))

Abstract

Evolutionary Computation (EC) introduces a new paradigm for solving problems in Artificial Intelligence, representing solution candidates as individuals and evolving them based on Darwin’s Theory of Natural Selection. Genetic Algorithms (GA) and Genetic Programming (GP), two important EC techniques, have been successfully applied both in theoretical scenarios and practical situations. This work discusses an issue of great relevance and impact on this type of algorithm: the automatic adjustment of the parameters that control the search process. Based on a recent research, a method that controls the population size in a GA is adapted and implemented in GP. A series of classic experiments has been performed before and after the modifications, showing that this method can improve the algorithms’ robustness and reliability. The data allow a discussion about the method and the importance of the adaptation of parameters in EC algorithms.

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Author information

Authors and Affiliations

  1. Computer Science Department, Federal University of Paraná (UFPR), P.O. Box 19081, Zip Code 81531-990, Curitiba, PR, Brazil

    Eduardo Spinosa & Aurora Pozo

Authors
  1. Eduardo Spinosa
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  2. Aurora Pozo
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Editor information

Editors and Affiliations

  1. Departamento de Automação e Sistemas, Universidade Federal de Santa Catarina, 88040-900, Florianópolis, SC, Brazil

    Guilherme Bittencourt

  2. Centro de Informática, Universidade Federal de Pernambuco, Cx. Postal 7851, 50732-970, Recife, PE, Brazil

    Geber L. Ramalho

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© 2002 Springer-Verlag Berlin Heidelberg

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Spinosa, E., Pozo, A. (2002). Controlling the Population Size in Genetic Programming. In: Bittencourt, G., Ramalho, G.L. (eds) Advances in Artificial Intelligence. SBIA 2002. Lecture Notes in Computer Science(), vol 2507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36127-8_33

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  • DOI: https://doi.org/10.1007/3-540-36127-8_33

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00124-9

  • Online ISBN: 978-3-540-36127-5

  • eBook Packages: Springer Book Archive

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