Local Search is Underused in Genetic Programming

Bibliographic Details
Main Author: Trujillo, Leonardo
Publication Date: 2018
Other Authors: Z-Flores, Emigdio, Juárez-Smith, Perla S., Legrand, Pierrick, Silva, Sara, Castelli, Mauro, Vanneschi, Leonardo, Schütze, Oliver, Muñoz, Luis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/152717
Summary: Trujillo, L., Z-Flores, E., Juárez-Smith, P. S., Legrand, P., Silva, S., Castelli, M., ... Muñoz, L. (2018). Local Search is Underused in Genetic Programming. In R. Riolo, B. Worzel, B. Goldman, & B. Tozier (Eds.), Genetic Programming Theory and Practice XIV (pp. 119-137). [8] (Genetic and Evolutionary Computation). Springer. https://doi.org/10.1007/978-3-319-97088-2_8
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spelling Local Search is Underused in Genetic ProgrammingGenetic programming (GP)EvolvabilityLocal search (optimization)Symbolic regressionNumerical optimizationBloatNeuroEvolution of augmenting topologiesTrujillo, L., Z-Flores, E., Juárez-Smith, P. S., Legrand, P., Silva, S., Castelli, M., ... Muñoz, L. (2018). Local Search is Underused in Genetic Programming. In R. Riolo, B. Worzel, B. Goldman, & B. Tozier (Eds.), Genetic Programming Theory and Practice XIV (pp. 119-137). [8] (Genetic and Evolutionary Computation). Springer. https://doi.org/10.1007/978-3-319-97088-2_8There 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.SpringerNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNTrujillo, LeonardoZ-Flores, EmigdioJuárez-Smith, Perla S.Legrand, PierrickSilva, SaraCastelli, MauroVanneschi, LeonardoSchütze, OliverMuñoz, Luis2023-05-12T22:03:49Z2018-10-252018-10-25T00:00:00Zbook partinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10362/152717eng978-3-319-97087-51932-0167PURE: 13174756https://doi.org/10.1007/978-3-319-97088-2_8info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:11:26Zoai:run.unl.pt:10362/152717Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:42:00.013748Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Local Search is Underused in Genetic Programming
title Local Search is Underused in Genetic Programming
spellingShingle Local Search is Underused in Genetic Programming
Trujillo, Leonardo
Genetic programming (GP)
Evolvability
Local search (optimization)
Symbolic regression
Numerical optimization
Bloat
NeuroEvolution of augmenting topologies
title_short Local Search is Underused in Genetic Programming
title_full Local Search is Underused in Genetic Programming
title_fullStr Local Search is Underused in Genetic Programming
title_full_unstemmed Local Search is Underused in Genetic Programming
title_sort Local Search is Underused in Genetic Programming
author Trujillo, Leonardo
author_facet Trujillo, Leonardo
Z-Flores, Emigdio
Juárez-Smith, Perla S.
Legrand, Pierrick
Silva, Sara
Castelli, Mauro
Vanneschi, Leonardo
Schütze, Oliver
Muñoz, Luis
author_role author
author2 Z-Flores, Emigdio
Juárez-Smith, Perla S.
Legrand, Pierrick
Silva, Sara
Castelli, Mauro
Vanneschi, Leonardo
Schütze, Oliver
Muñoz, Luis
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Trujillo, Leonardo
Z-Flores, Emigdio
Juárez-Smith, Perla S.
Legrand, Pierrick
Silva, Sara
Castelli, Mauro
Vanneschi, Leonardo
Schütze, Oliver
Muñoz, Luis
dc.subject.por.fl_str_mv Genetic programming (GP)
Evolvability
Local search (optimization)
Symbolic regression
Numerical optimization
Bloat
NeuroEvolution of augmenting topologies
topic Genetic programming (GP)
Evolvability
Local search (optimization)
Symbolic regression
Numerical optimization
Bloat
NeuroEvolution of augmenting topologies
description Trujillo, L., Z-Flores, E., Juárez-Smith, P. S., Legrand, P., Silva, S., Castelli, M., ... Muñoz, L. (2018). Local Search is Underused in Genetic Programming. In R. Riolo, B. Worzel, B. Goldman, & B. Tozier (Eds.), Genetic Programming Theory and Practice XIV (pp. 119-137). [8] (Genetic and Evolutionary Computation). Springer. https://doi.org/10.1007/978-3-319-97088-2_8
publishDate 2018
dc.date.none.fl_str_mv 2018-10-25
2018-10-25T00:00:00Z
2023-05-12T22:03:49Z
dc.type.driver.fl_str_mv book part
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/152717
url http://hdl.handle.net/10362/152717
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-319-97087-5
1932-0167
PURE: 13174756
https://doi.org/10.1007/978-3-319-97088-2_8
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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