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EPIA Conference on Artificial Intelligence

EPIA 2019: Progress in Artificial Intelligence pp 775–787Cite as

Extending Local Search in Geometric Semantic Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11804))

Abstract

In this paper we continue the investigation of the effect of local search in geometric semantic genetic programming (GSGP), with the introduction of a new general local search operator that can be easily customized. We show that it is able to obtain results on par with the current best-performing GSGP with local search and, in most cases, better than standard GSGP.

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Acknowledgments

This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project DSAIPA/DS/0022/2018 (GADgET).

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Authors and Affiliations

  1. NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal

    Mauro Castelli & Martina Saletta

  2. Dipartimento di Informatica Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano Bicocca, Viale Sarca 336, 20126, Milan, Italy

    Luca Manzoni & Luca Mariot

Authors
  1. Mauro Castelli
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  2. Luca Manzoni
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  3. Luca Mariot
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  4. Martina Saletta
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Corresponding author

Correspondence to Luca Mariot .

Editor information

Editors and Affiliations

  1. INESC-TEC, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Moura Oliveira

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. LIACC/UP, University of Porto, Porto, Portugal

    Luís Paulo Reis

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Castelli, M., Manzoni, L., Mariot, L., Saletta, M. (2019). Extending Local Search in Geometric Semantic Genetic Programming. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_64

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  • DOI: https://doi.org/10.1007/978-3-030-30241-2_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30240-5

  • Online ISBN: 978-3-030-30241-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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