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Local Search is Underused in Genetic Programming

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Genetic Programming Theory and Practice XIV

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

There are two important limitations of standard tree-based genetic programming (GP). First, GP tends to evolve unnecessarily large programs, what is referred to as bloat. Second, GP uses inefficient search operators that focus on modifying program syntax. The first problem has been studied extensively, with many works proposing bloat control methods. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators, to improve convergence. In this work, our goal is to experimentally show that both problems can be effectively addressed by incorporating a local search optimizer as an additional search operator. Using real-world problems, we show that this rather simple strategy can improve the convergence and performance of tree-based GP, while also reducing program size. Given these results, a question arises: Why are local search strategies so uncommon in GP? A small survey of popular GP libraries suggests to us that local search is underused in GP systems. We conclude by outlining plausible answers for this question and highlighting future work.

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Notes

  1. 1.

    We will use the terms “standard GP” and “tree-based GP” interchangeably in this work, referring to the basic GP algorithm that relies on a tree representation and subtree genetic operators.

  2. 2.

    http://www.tree-lab.org/index.php/resources-2/downloads/open-source-tools/item/145-neat-gp.

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Acknowledgements

Funding for this work was provided by CONACYT Basic Science Research Project No. 178323 and Fronteras de la Ciencia FC-2015-2/944, TecNM (México) Research Projects 5414.14-P and 5621.15-P, and by the FP7 Marie Curie-IRSES 2013 European Commission program through project ACoBSEC with contract 439 No. 612689

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Authors and Affiliations

  1. Tecnológico Nacional de México/I.T. Tijuana, Tijuana, B.C., Mexico

    Leonardo Trujillo, Emigdio Z-Flores, Perla S. Juárez-Smith & Luis Muñoz

  2. Universitè de Bordeaux, Institut de Mathèmatiques de Bordeaux, UMR CNRS 5251, Bordeaux, France

    Pierrick Legrand

  3. CQFD Team, Inria Bordeaux Sud-Ouest, Talence, France

    Pierrick Legrand

  4. BioISI Biosystems and Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal

    Sara Silva

  5. NOVA IMS, Universidade Nova de Lisboa, Lisbon, Portugal

    Mauro Castelli & Leonardo Vanneschi

  6. Computer Science Department, CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

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Correspondence to Leonardo Trujillo .

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  3. Colorado State University, Fort Collins, CO, USA

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Trujillo, L. et al. (2018). Local Search is Underused in Genetic Programming. In: Riolo, R., Worzel, B., Goldman, B., Tozier, B. (eds) Genetic Programming Theory and Practice XIV. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-97088-2_8

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