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ESAGP – A Semantic GP Framework Based on Alignment in the Error Space

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Book cover Genetic Programming (EuroGP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8599))

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Abstract

This paper introduces the concepts of error vector and error space, directly bound to semantics, one of the hottest topics in genetic programming. Based on these concepts, we introduce the notions of optimally aligned individuals and optimally coplanar individuals. We show that, given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. Thus, we introduce a genetic programming framework for symbolic regression called Error Space Alignment GP (ESAGP) and two of its instances: ESAGP-1, whose objective is to find optimally aligned individuals, and ESAGP-2, whose objective is to find optimally coplanar individuals. We also discuss how to generalize the approach to any number of dimensions. Using two complex real-life applications, we provide experimental evidence that ESAGP-2 outperforms ESAGP-1, which in turn outperforms both standard GP and geometric semantic GP. This suggests that “adding dimensions” is beneficial and encourages us to pursue the study in many different directions, that we summarize in the final part of the manuscript.

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Author information

Authors and Affiliations

  1. GSSI, Gran Sasso Science Institute, INFN, 67100, L’Aquila, Italy

    Stefano Ruberto

  2. INESC-ID, IST, University of Lisbon, 1000-029, Lisbon, Portugal

    Stefano Ruberto & Sara Silva

  3. ISEGI, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal

    Leonardo Vanneschi & Mauro Castelli

  4. LabMAg, FCUL, University of Lisbon, 1749-016, Lisbon, Portugal

    Sara Silva

  5. CISUC, Universidade de Coimbra, 3030-290, Coimbra, Portugal

    Sara Silva

Authors
  1. Stefano Ruberto
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  2. Leonardo Vanneschi
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  3. Mauro Castelli
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  4. Sara Silva
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Editor information

Editors and Affiliations

  1. University College Dublin, UCD CASL, Belfield, Dublin 4, Ireland

    Miguel Nicolau

  2. Institute of Computing Science, Poznan University of Technology, Piotowo 2, 60-965, Poznań, Poland

    Krzysztof Krawiec

  3. Faculty of Computer Science, Dalhousie University, University Avenue 6050, B3H 1W5, Halifax, NS, Canada

    Malcolm I. Heywood

  4. Instituto Superior de Estatística e Gestão de Informaticão, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal

    Mauro Castelli

  5. Departamento de Arquitectura y Tecnología de Computadores (ETS), Universidad de Granada, 18071, Granada, Spain

    Pablo García-Sánchez  & Juan J. Merelo  & 

  6. Departamento de Informática, Universidad de Jaén, 23071, Jaén, Spain

    Victor M. Rivas Santos

  7. Institute for Informatics and Digital Innovation, Edinburgh Napier University, Merchiston Campus, EH10 5DT, Edinburgh, UK

    Kevin Sim

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Ruberto, S., Vanneschi, L., Castelli, M., Silva, S. (2014). ESAGP – A Semantic GP Framework Based on Alignment in the Error Space. In: Nicolau, M., et al. Genetic Programming. EuroGP 2014. Lecture Notes in Computer Science, vol 8599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44303-3_13

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  • DOI: https://doi.org/10.1007/978-3-662-44303-3_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44302-6

  • Online ISBN: 978-3-662-44303-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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