dc.creator | Giráldez Rojo, Raúl | es |
dc.creator | Aguilar Ruiz, Jesús Salvador | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-07-06T09:02:24Z | |
dc.date.available | 2016-07-06T09:02:24Z | |
dc.date.issued | 2005 | |
dc.identifier.citation | Giráldez Rojo, R., Aguilar Ruiz, J.S. y Riquelme Santos, J.C. (2005). Knowledge-Based Fast Evaluation for Evolutionary Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35 (2), 254-261. | |
dc.identifier.issn | 1094-6977 | es |
dc.identifier.uri | http://hdl.handle.net/11441/43224 | |
dc.description.abstract | The increasing amount of information available is encouraging
the search for efficient techniques to improve the data mining
methods, especially those which consume great computational resources,
such as evolutionary computation.Efficacy and efficiency are two critical
aspects for knowledge-based techniques.The incorporation of knowledge
into evolutionary algorithms (EAs) should provide either better solutions
(efficacy) or the equivalent solutions in shorter time (efficiency), regarding
the same evolutionary algorithm without incorporating such knowledge.
In this paper, we categorize and summarize some of the incorporation of
knowledge techniques for evolutionary algorithms and present a novel
data structure, called efficient evaluation structure (EES), which helps the
evolutionary algorithm to provide decision rules using less computational
resources.The EES-based EA is tested and compared to another EA
system and the experimental results show the quality of our approach,
reducing the computational cost about 50%, maintaining the global
accuracy of the final set of decision rules. | es |
dc.description.sponsorship | CICYT TIN2004-00159 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | IEEE | es |
dc.relation.ispartof | IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35 (2), 254-261. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Data structures | es |
dc.subject | evolutionary algorithms (EAs) | es |
dc.subject | knowledge incorporation | es |
dc.subject | supervised learning | es |
dc.title | Knowledge-Based Fast Evaluation for Evolutionary Learning | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2004-00159 | es |
dc.identifier.doi | http://dx.doi.org/10.1109/TSMCC.2004.841904 | es |
idus.format.extent | 8 | es |
dc.journaltitle | IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) | es |
dc.publication.volumen | 35 | es |
dc.publication.issue | 2 | es |
dc.publication.initialPage | 254 | es |
dc.publication.endPage | 261 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/43224 | |
dc.contributor.funder | Comisión Interministerial de Ciencia y Tecnología (CICYT). España | |