Publicação
Genetic programming with semantic equivalence classes
| Resumo: | In 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. |
|---|---|
| Autores principais: | Ruberto, Stefano |
| Outros Autores: | Vanneschi, Leonardo; Castelli, Mauro |
| Assunto: | Equivalence classes Genetic programming Semantics General Computer Science General Mathematics |
| Ano: | 2019 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1865920626398593024 |
|---|---|
| author | Ruberto, Stefano |
| author2 | Vanneschi, Leonardo Castelli, Mauro |
| author2_role | author author |
| author_facet | Ruberto, Stefano Ruberto, Stefano Vanneschi, Leonardo Castelli, Mauro Vanneschi, Leonardo Castelli, Mauro |
| author_role | author |
| contributor_name_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School Elsevier RUN |
| country_str | PT |
| creators_json_str | [{\"Person.name\":\"Ruberto, Stefano\"},{\"Person.name\":\"Vanneschi, Leonardo\"},{\"Person.name\":\"Castelli, Mauro\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School Elsevier RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Ruberto, Stefano Vanneschi, Leonardo Castelli, Mauro |
| datacite.date.Accepted.fl_str_mv | 2019-02-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-01-27T01:32:02Z |
| datacite.date.embargoed.fl_str_mv | 2024-01-27T01:32:02Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Equivalence classes Genetic programming Semantics General Computer Science General Mathematics |
| datacite.titles.title.fl_str_mv | Genetic programming with semantic equivalence classes |
| dc.contributor.none.fl_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School Elsevier RUN |
| dc.creator.none.fl_str_mv | Ruberto, Stefano Vanneschi, Leonardo Castelli, Mauro |
| dc.date.Accepted.fl_str_mv | 2019-02-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-01-27T01:32:02Z |
| dc.date.embargoed.fl_str_mv | 2024-01-27T01:32:02Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/151422 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Equivalence classes Genetic programming Semantics General Computer Science General Mathematics |
| dc.title.fl_str_mv | Genetic programming with semantic equivalence classes |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | In 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/21747b94-066f-4441-beeb-891f6e26c3f5/download |
| id | run_e89a4ed9a8df3bbe736f80d4eaa5613d |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/151422 |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/151422 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Ruberto, Stefano Vanneschi, Leonardo Castelli, Mauro |
| publishDate | 2019 |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engenIn 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.application/pdfenGenetic programming with semantic equivalence classesRuberto, StefanoVanneschi, LeonardoCastelli, MauroNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolElsevierHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptISSNIsPartOf2210-6502URNIsPartOfPURE: 5097819URNIsPartOfPURE UUID: 6c83e125-f582-4e11-af92-99393889f5bfURNIsPartOfScopus: 85048885945URNIsPartOfWOS: 000456761600032URNIsPartOfORCID: /0000-0002-8793-1451/work/131992546URNIsPartOfORCID: /0000-0003-4732-3328/work/151426701DOIIsPartOf10.1016/j.swevo.2018.06.0012024-01-27T01:32:02Z2019-022019-02-01T00:00:00ZHandlehttp://hdl.handle.net/10362/151422http://purl.org/coar/access_right/c_abf2open accessEquivalence classesGenetic programmingSemanticsGeneral Computer ScienceGeneral Mathematics830260 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/21747b94-066f-4441-beeb-891f6e26c3f5/download |
| spellingShingle | Genetic programming with semantic equivalence classes Genetic programming with semantic equivalence classes Ruberto, Stefano Equivalence classes Genetic programming Semantics General Computer Science General Mathematics Ruberto, Stefano Equivalence classes Genetic programming Semantics General Computer Science General Mathematics |
| status | NEW |
| subject.fl_str_mv | Equivalence classes Genetic programming Semantics General Computer Science General Mathematics |
| title | Genetic programming with semantic equivalence classes |
| title_full | Genetic programming with semantic equivalence classes |
| title_fullStr | Genetic programming with semantic equivalence classes Genetic programming with semantic equivalence classes |
| title_full_unstemmed | Genetic programming with semantic equivalence classes Genetic programming with semantic equivalence classes |
| title_short | Genetic programming with semantic equivalence classes |
| title_sort | Genetic programming with semantic equivalence classes |
| topic | Equivalence classes Genetic programming Semantics General Computer Science General Mathematics |
| topic_facet | Equivalence classes Genetic programming Semantics General Computer Science General Mathematics |
| url | http://hdl.handle.net/10362/151422 |
| visible | 1 |