Alignment-based genetic programming for real life applications
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Publication Date: | 2019 |
Other Authors: | , , |
Format: | Article |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | https://doi.org/10.1016/j.swevo.2018.09.006 |
Summary: | Vanneschi, L., Castelli, M., Scott, K., & Trujillo, L. (2019). Alignment-based genetic programming for real life applications. Swarm and Evolutionary Computation, 44(February), 840-851. DOI: 10.1016/j.swevo.2018.09.006 |
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Alignment-based genetic programming for real life applicationsAlignmentError spaceGenetic programmingGeometric semantic operatorsReal-life applicationsComputer Science(all)Mathematics(all)Vanneschi, L., Castelli, M., Scott, K., & Trujillo, L. (2019). Alignment-based genetic programming for real life applications. Swarm and Evolutionary Computation, 44(February), 840-851. DOI: 10.1016/j.swevo.2018.09.006A recent discovery has attracted the attention of many researchers in the field of genetic programming: given individuals with particular characteristics of alignment in the error space, called optimally aligned, it is possible to reconstruct a globally optimal solution. Furthermore, recent preliminary experiments have shown that an indirect search consisting of looking for optimally aligned individuals can have benefits in terms of generalization ability compared to a direct search for optimal solutions. For this reason, defining genetic programming systems that look for optimally aligned individuals is becoming an ambitious and important objective. Nevertheless, the systems that have been introduced so far present important limitations that make them unusable in practice, particularly for complex real-life applications. In this paper, we overcome those limitations, and we present the first usable alignment-based genetic programming system, called nested alignment genetic programming (NAGP). The presented experimental results show that NAGP is able to outperform two of the most recognized state-of-the-art genetic programming systems on four complex real-life applications. The predictive models generated by NAGP are not only more effective than the ones produced by the other studied methods but also significantly smaller and thus more manageable and interpretable.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNVanneschi, LeonardoCastelli, MauroScott, KristenTrujillo, Leonardo2024-01-27T01:32:02Z2019-022019-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttps://doi.org/10.1016/j.swevo.2018.09.006eng2210-6502PURE: 6081854http://www.scopus.com/inward/record.url?scp=85054516429&partnerID=8YFLogxKhttps://doi.org/10.1016/j.swevo.2018.09.006info: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:RCAAP2024-05-22T17:35:11Zoai:run.unl.pt:10362/49618Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:06:08.751395Repositórios 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 |
Alignment-based genetic programming for real life applications |
title |
Alignment-based genetic programming for real life applications |
spellingShingle |
Alignment-based genetic programming for real life applications Vanneschi, Leonardo Alignment Error space Genetic programming Geometric semantic operators Real-life applications Computer Science(all) Mathematics(all) |
title_short |
Alignment-based genetic programming for real life applications |
title_full |
Alignment-based genetic programming for real life applications |
title_fullStr |
Alignment-based genetic programming for real life applications |
title_full_unstemmed |
Alignment-based genetic programming for real life applications |
title_sort |
Alignment-based genetic programming for real life applications |
author |
Vanneschi, Leonardo |
author_facet |
Vanneschi, Leonardo Castelli, Mauro Scott, Kristen Trujillo, Leonardo |
author_role |
author |
author2 |
Castelli, Mauro Scott, Kristen Trujillo, Leonardo |
author2_role |
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 |
Vanneschi, Leonardo Castelli, Mauro Scott, Kristen Trujillo, Leonardo |
dc.subject.por.fl_str_mv |
Alignment Error space Genetic programming Geometric semantic operators Real-life applications Computer Science(all) Mathematics(all) |
topic |
Alignment Error space Genetic programming Geometric semantic operators Real-life applications Computer Science(all) Mathematics(all) |
description |
Vanneschi, L., Castelli, M., Scott, K., & Trujillo, L. (2019). Alignment-based genetic programming for real life applications. Swarm and Evolutionary Computation, 44(February), 840-851. DOI: 10.1016/j.swevo.2018.09.006 |
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 |
https://doi.org/10.1016/j.swevo.2018.09.006 |
url |
https://doi.org/10.1016/j.swevo.2018.09.006 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2210-6502 PURE: 6081854 http://www.scopus.com/inward/record.url?scp=85054516429&partnerID=8YFLogxK https://doi.org/10.1016/j.swevo.2018.09.006 |
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openAccess |
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12 application/pdf |
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