Genetic programming with semantic equivalence classes

Detalhes bibliográficos
Autor(a) principal: Ruberto, Stefano
Data de Publicação: 2019
Outros Autores: Vanneschi, Leonardo, Castelli, Mauro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/151422
Resumo: Ruberto, S., Vanneschi, L., & Castelli, M. (2019). Genetic programming with semantic equivalence classes. Swarm and Evolutionary Computation, 44(February), 453-469. DOI: 10.1016/j.swevo.2018.06.001
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spelling Genetic programming with semantic equivalence classesEquivalence classesGenetic programmingSemanticsComputer Science(all)Mathematics(all)Ruberto, S., Vanneschi, L., & Castelli, M. (2019). Genetic programming with semantic equivalence classes. Swarm and Evolutionary Computation, 44(February), 453-469. DOI: 10.1016/j.swevo.2018.06.001In this paper, we introduce the concept of semantics-based equivalence classes for symbolic regression problems in genetic programming. The idea is implemented by means of two different genetic programming systems, in which two different definitions of equivalence are used. In both systems, whenever a solution in an equivalence class is found, it is possible to generate any other solution in that equivalence class analytically. As such, these two systems allow us to shift the objective of genetic programming: instead of finding a globally optimal solution, the objective is now to find any solution that belongs to the same equivalence class as a global optimum. Further, we propose improvements to these genetic programming systems in which, once a solution that belongs to a particular equivalence class is generated, no other solution in that class is accepted in the population during the evolution anymore. We call these improved versions filtered systems. Experimental results obtained via seven complex real-life test problems show that using equivalence classes is a promising idea and that filters are generally helpful for improving the systems' performance. Furthermore, the proposed methods produce individuals with a much smaller size with respect to geometric semantic genetic programming. Finally, we show that filters are also useful to improve the performance of a state-of-the-art method, not explicitly based on semantic equivalence classes, like linear scaling.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNRuberto, StefanoVanneschi, LeonardoCastelli, Mauro2024-01-27T01:32:02Z2019-022019-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttp://hdl.handle.net/10362/151422eng2210-6502PURE: 5097819https://doi.org/10.1016/j.swevo.2018.06.001info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:33:52Zoai:run.unl.pt:10362/151422Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:35.205628Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Genetic programming with semantic equivalence classes
title Genetic programming with semantic equivalence classes
spellingShingle Genetic programming with semantic equivalence classes
Ruberto, Stefano
Equivalence classes
Genetic programming
Semantics
Computer Science(all)
Mathematics(all)
title_short Genetic programming with semantic equivalence classes
title_full Genetic programming with semantic equivalence classes
title_fullStr Genetic programming with semantic equivalence classes
title_full_unstemmed Genetic programming with semantic equivalence classes
title_sort Genetic programming with semantic equivalence classes
author Ruberto, Stefano
author_facet Ruberto, Stefano
Vanneschi, Leonardo
Castelli, Mauro
author_role author
author2 Vanneschi, Leonardo
Castelli, Mauro
author2_role 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 Ruberto, Stefano
Vanneschi, Leonardo
Castelli, Mauro
dc.subject.por.fl_str_mv Equivalence classes
Genetic programming
Semantics
Computer Science(all)
Mathematics(all)
topic Equivalence classes
Genetic programming
Semantics
Computer Science(all)
Mathematics(all)
description Ruberto, S., Vanneschi, L., & Castelli, M. (2019). Genetic programming with semantic equivalence classes. Swarm and Evolutionary Computation, 44(February), 453-469. DOI: 10.1016/j.swevo.2018.06.001
publishDate 2019
dc.date.none.fl_str_mv 2019-02
2019-02-01T00:00:00Z
2024-01-27T01:32:02Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/151422
url http://hdl.handle.net/10362/151422
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2210-6502
PURE: 5097819
https://doi.org/10.1016/j.swevo.2018.06.001
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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