A comparative analysis of evolutionary learning in artificial hydrocarbon networks
| dc.contributor.author | Ponce, Hiram | |
| dc.contributor.other | Campus Ciudad de México | es |
| dc.creator | HIRAM EREDIN PONCE ESPINOSA;376768 | |
| dc.date.accessioned | 2022-01-31T19:37:37Z | |
| dc.date.available | 2022-01-31T19:37:37Z | |
| dc.date.issued | 2020-10-07 | |
| dc.identifier.citation | Ponce 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_17 | en |
| dc.identifier.isbn | 9783030608835 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12552/5877 | |
| dc.identifier.uri | https://doi.org/10.1007/978-3-030-60884-2_17 | |
| dc.description.abstract | Artificial 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.tableofcontents | 1 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 Conclusions | en |
| dc.language.iso | eng | en |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | en |
| dc.relation.ispartof | REPOSITORIO SCRIPTA | es |
| dc.relation.ispartof | OPENAIRE | es |
| dc.rights | Acceso Embargado | es |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0 | |
| dc.rights.uri | https://v2.sherpa.ac.uk/id/publication/33095 | |
| dc.source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en |
| dc.subject | Artificial intelligence | en |
| dc.subject | Classification (of information) | en |
| dc.subject | Heuristic algorithms | en |
| dc.subject | Heuristic methods | en |
| dc.subject | Hydrocarbons | en |
| dc.subject | Soft computing | en |
| dc.subject | Binary classification problems | en |
| dc.subject | Comparative analysis | en |
| dc.subject | Evolutionary Learning | en |
| dc.subject | Harmony search algorithms | en |
| dc.subject | Meta-heuristic optimizations | en |
| dc.subject | Optimization algorithms | en |
| dc.subject | Regression problem | en |
| dc.subject | Training algorithms | en |
| dc.subject | Particle swarm optimization (PSO) | en |
| dc.subject | Artificial organic networks | en |
| dc.subject | Classification | en |
| dc.subject | Machine learning | en |
| dc.subject | Meta-heuristic optimization | en |
| dc.subject | Regression | en |
| dc.subject | Supervised learning | en |
| dc.subject.classification | INGENIERÍA Y TECNOLOGÍA | en |
| dc.subject.classification | Ingeniería | es |
| dc.title | A comparative analysis of evolutionary learning in artificial hydrocarbon networks | en |
| dc.type | Contribución a congreso | es |
| dcterms.audience | Investigadores | es |
| dcterms.audience | Maestros | es |
| dcterms.audience | Estudiantes | es |
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| dc.description.version | Versión aceptada | es |
| dc.identifier.doi | https://doi.org/10.1007/978-3-030-60884-2_17 | |
| dc.identifier.pagenumber | 223-234 |
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