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European Conference on Genetic Programming

EuroGP 2018: Genetic Programming pp 166–183Cite as

A Multiple Expression Alignment Framework for Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10781))

Abstract

Alignment in the error space is a recent idea to exploit semantic awareness in genetic programming. In a previous contribution, the concepts of optimally aligned and optimally coplanar individuals were introduced, and it was shown that given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. As a consequence, genetic programming methods, aimed at searching for optimally aligned, or optimally coplanar, individuals were introduced. In this paper, we critically discuss those methods, analyzing their major limitations and we propose new genetic programming systems aimed at overcoming those limitations. The presented experimental results, conducted on four real-life symbolic regression problems, show that the proposed algorithms outperform not only the existing methods based on the concept of alignment in the error space, but also geometric semantic genetic programming and standard genetic programming.

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Authors and Affiliations

  1. NOVA IMS, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal

    Leonardo Vanneschi, Kristen Scott & Mauro Castelli

Authors
  1. Leonardo Vanneschi
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  2. Kristen Scott
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  3. Mauro Castelli
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Corresponding author

Correspondence to Leonardo Vanneschi .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Lisbon, Portugal

    Mauro Castelli

  2. Brno University of Technology, Brno, Czech Republic

    Lukas Sekanina

  3. Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. University of Parma, Parma, Italy

    Stefano Cagnoni

  5. University of Cádiz, Cádiz, Spain

    Pablo García-Sánchez

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Vanneschi, L., Scott, K., Castelli, M. (2018). A Multiple Expression Alignment Framework for Genetic Programming. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science(), vol 10781. Springer, Cham. https://doi.org/10.1007/978-3-319-77553-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-77553-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77552-4

  • Online ISBN: 978-3-319-77553-1

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