dor_id: 45649

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336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/299/296

100.1.#.a: Kim, D.W.; Ko, S.; Kang, B.Y. Kang

524.#.#.a: Kim, D.W., et al. (2013). Structure Learning of Bayesian Networks by Estimation of Distribution Algorithms with Transpose Mutation. Journal of Applied Research and Technology; Vol. 11 Núm. 4. Recuperado de https://repositorio.unam.mx/contenidos/45649

245.1.0.a: Structure Learning of Bayesian Networks by Estimation of Distribution Algorithms with Transpose Mutation

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2013

264.#.1.c: 2013-08-01

653.#.#.a: Estimation of distribution algorithms; Mutation; Bayesian network; Structure learning; Optimization

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041.#.7.h: eng

520.3.#.a: Estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms that were developed as a natural alternative to genetic algorithms (GAs). Several studies have demonstrated that the heuristic scheme of EDAs is effective and efficient for many optimization problems. Recently, it has been reported that the incorporation of mutation into EDAs increases the diversity of genetic information in the population, thereby avoiding premature convergence into a suboptimal solution. In this study, we propose a new mutation operator, a transpose mutation, designed for Bayesian structure learning. It enhances the diversity of the offspring and it increases the possibility of inferring the correct arc direction by considering the arc directions in candidate solutions as bi-directional, using the matrix transpose operator. As compared to the conventional EDAs, the transpose mutation-adopted EDAs are superior and effective algorithms for learning Bayesian networks.

773.1.#.t: Journal of Applied Research and Technology; Vol. 11 Núm. 4

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

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264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.1016/S1665-6423(13)71566-9

harvesting_date: 2023-11-08 13:10:00.0

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last_modified: 2024-03-19 14:00:00

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Artículo

Structure Learning of Bayesian Networks by Estimation of Distribution Algorithms with Transpose Mutation

Kim, D.W.; Ko, S.; Kang, B.Y. Kang

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Kim, D.W., et al. (2013). Structure Learning of Bayesian Networks by Estimation of Distribution Algorithms with Transpose Mutation. Journal of Applied Research and Technology; Vol. 11 Núm. 4. Recuperado de https://repositorio.unam.mx/contenidos/45649

Descripción del recurso

Autor(es)
Kim, D.W.; Ko, S.; Kang, B.Y. Kang
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Structure Learning of Bayesian Networks by Estimation of Distribution Algorithms with Transpose Mutation
Fecha
2013-08-01
Resumen
Estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms that were developed as a natural alternative to genetic algorithms (GAs). Several studies have demonstrated that the heuristic scheme of EDAs is effective and efficient for many optimization problems. Recently, it has been reported that the incorporation of mutation into EDAs increases the diversity of genetic information in the population, thereby avoiding premature convergence into a suboptimal solution. In this study, we propose a new mutation operator, a transpose mutation, designed for Bayesian structure learning. It enhances the diversity of the offspring and it increases the possibility of inferring the correct arc direction by considering the arc directions in candidate solutions as bi-directional, using the matrix transpose operator. As compared to the conventional EDAs, the transpose mutation-adopted EDAs are superior and effective algorithms for learning Bayesian networks.
Tema
Estimation of distribution algorithms; Mutation; Bayesian network; Structure learning; Optimization
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces