Analyzing the k most probable solutions in EDAs based on bayesian networks
| dc.contributor.author | Echegoyen Arruti, Carlos | |
| dc.contributor.author | Mendiburu, Alexander | |
| dc.contributor.author | Santana, Roberto | |
| dc.contributor.author | Lozano, José Antonio | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.date.accessioned | 2025-01-17T11:48:38Z | |
| dc.date.available | 2025-01-17T11:48:38Z | |
| dc.date.issued | 2010 | |
| dc.date.updated | 2025-01-17T11:44:55Z | |
| dc.description | Acceso cerrado a este documento. No se encuentra disponible para la consulta pública. Depositado en Academica-e para cumplir con los requisitos de evaluación y acreditación académica del autor/a (sexenios, acreditaciones, etc.). | es_ES |
| dc.description.abstract | Estimation of distribution algorithms (EDAs) have been successfully applied to a wide variety of problems but, for themost complex approaches, there is no clear understanding of the way these algorithms complete the search. For that reason, in this work we exploit the probabilistic models that EDAs based on Bayesian networks are able to learn in order to provide new information about their behavior. Particularly, we analyze the k solutions with the highest probability in the distributions estimated during the search. In order to study the relationship between the probabilistic model and the fitness function, we focus on calculating, for the k most probable solutions (MPSs), the probability values, the function values and the correlation between both sets of values at each step of the algorithm. Furthermore, the objective functions of the k MPSs are contrasted with the k best individuals in the population. We complete the analysis by calculating the position of the optimum in the k MPSs during the search and the genotypic diversity of these solutions. We carry out the analysis by optimizing functions of different natures such as Trap5, two variants of Ising spin glass and Max-SAT. The results not only show information about the relationship between the probabilistic model and the fitness function, but also allow us to observe characteristics of the search space, the quality of the setup of the parameters and even distinguish between successful and unsuccessful runs. | en |
| dc.description.sponsorship | This work has been partially supported by the Saiotek and Research Groups 2007-2012 (IT-242-07) programs (Basque Government), TIN2008-06815-C02-01 and Consolider Ingenio 2010 - CSD2007-00018 projects (Spanish Ministry of Science and Innovation) and COMBIOMED network in computational biomedicine (Carlos III Health Institute). Carlos Echegoyen has a grant from UPV-EHU. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Echegoyen-Arruti, C., Mendiburu, A., Santana, R., Lozano, J. A. (2010) Analyzing the k most probable solutions in EDAs based on bayesian networks. In Chen Y. (Ed.), Exploitation of linkage learning in evolutionary algorithms (pp. 163-189). Springer. 978-3-642-12834-9 | |
| dc.identifier.doi | 10.1007/978-3-642-12834-9_8 | |
| dc.identifier.isbn | 978-3-642-12834-9 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/52987 | |
| dc.language.iso | eng | |
| dc.publisher | Springer | |
| dc.relation.ispartof | In Chen, Y. (Ed.). Exploitation of linkage learning in evolutionary algorithms. Berlín: Springer; 2010. p. 163-189 | |
| dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//TIN2008-06815-C02-01/ES/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/MEC//CSD2007-00018/ES/ | |
| dc.relation.publisherversion | https://doi.org/10.1007/978-3-642-12834-9_8 | |
| dc.rights | © 2010 Springer-Verlag Berlin Heidelberg | |
| dc.rights.accessRights | info:eu-repo/semantics/closedAccess | |
| dc.subject | Bayesian network | en |
| dc.subject | Spin glass | en |
| dc.subject | Good individual | en |
| dc.subject | Conjunctive normal form | en |
| dc.subject | Generation generation | en |
| dc.title | Analyzing the k most probable solutions in EDAs based on bayesian networks | en |
| dc.type | info:eu-repo/semantics/bookPart | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | cb45e960-5f19-491c-ba50-555e4ce5f169 | |
| relation.isAuthorOfPublication.latestForDiscovery | cb45e960-5f19-491c-ba50-555e4ce5f169 |