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dc.contributor.authorPonce, Hiram
dc.contributor.otherCampus Ciudad de Méxicoes
dc.creatorHIRAM EREDIN PONCE ESPINOSA;376768
dc.date.accessioned2022-01-31T19:37:37Z
dc.date.available2022-01-31T19:37:37Z
dc.date.issued2020-10-07
dc.identifier.citationPonce H., Souza P. (2020) A Comparative Analysis of Evolutionary Learning in Artificial Hydrocarbon Networks. In: Martínez-Villaseñor L., Herrera-Alcántara O., Ponce H., Castro-Espinoza F.A. (eds) Advances in Soft Computing. MICAI 2020. Lecture Notes in Computer Science, vol 12468. Springer, Cham. https://doi.org/10.1007/978-3-030-60884-2_17en
dc.identifier.isbn9783030608835
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/20.500.12552/5877
dc.identifier.urihttps://doi.org/10.1007/978-3-030-60884-2_17
dc.description.abstractArtificial hydrocarbon networks (AHN) is a supervised learning model that is loosely inspired on the interactions of molecules in organic compounds. This method is able to model data in a hierarchical and robust way. However, the original training algorithm is very time-consuming. Recently, novel training algorithms have been applied, including evolutionary learning. Particularly, this training algorithm employed particle swarm optimization (PSO), as part of the procedure. In this paper, we present a benchmark of other meta-heuristic optimization algorithms implemented on the training method for AHN. In this study, PSO, harmony search algorithm, cuckoo search, grey wolf optimization and whale optimization algorithm, were tested. The experimental results were done using public data sets on regression and binary classification problems. From the results, we concluded that the best algorithm was cuckoo search optimization for regression problems, while there is no evidence that one of the algorithms performed better for binary classification problems. © 2020, Springer Nature Switzerland AG.en
dc.description.tableofcontents1 Introduction -- 2 Evolutionary Learning for Artificial Hydrocarbon Networks -- 2.1 Artificial Hydrocarbon Networks -- 2.2 Overview of Meta-heuristic Optimization Methods -- 3 Experimentation -- 4 Results and Discussion -- 5 Conclusionsen
dc.language.isoengen
dc.publisherSpringer Science and Business Media Deutschland GmbHen
dc.relation.ispartofREPOSITORIO SCRIPTAes
dc.relation.ispartofOPENAIREes
dc.rightsAcceso Embargadoes
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0
dc.rights.urihttps://v2.sherpa.ac.uk/id/publication/33095
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.subjectArtificial intelligenceen
dc.subjectClassification (of information)en
dc.subjectHeuristic algorithmsen
dc.subjectHeuristic methodsen
dc.subjectHydrocarbonsen
dc.subjectSoft computingen
dc.subjectBinary classification problemsen
dc.subjectComparative analysisen
dc.subjectEvolutionary Learningen
dc.subjectHarmony search algorithmsen
dc.subjectMeta-heuristic optimizationsen
dc.subjectOptimization algorithmsen
dc.subjectRegression problemen
dc.subjectTraining algorithmsen
dc.subjectParticle swarm optimization (PSO)en
dc.subjectArtificial organic networksen
dc.subjectClassificationen
dc.subjectMachine learningen
dc.subjectMeta-heuristic optimizationen
dc.subjectRegressionen
dc.subjectSupervised learningen
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAen
dc.subject.classificationIngenieríaes
dc.titleA comparative analysis of evolutionary learning in artificial hydrocarbon networksen
dc.typeContribución a congresoes
dcterms.audienceInvestigadoreses
dcterms.audienceMaestroses
dcterms.audienceEstudianteses
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dc.description.versionVersión aceptadaes
dc.identifier.doihttps://doi.org/10.1007/978-3-030-60884-2_17
dc.identifier.pagenumber223-234


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