Grammar-Guided Evolutionary Construction of Bayesian networks

Font Fernández, José María; Manrique Gamo, Daniel y Pascua Salvador, Eduardo (2011). Grammar-Guided Evolutionary Construction of Bayesian networks. En: "4th International Conference on Interplay Between Natural and Artificial Computation, IWINAC'11", 30/05/2011 - 03/06/2011, Las Palmas de Gran Canaria, España. ISBN 978-3-642-21343-4.

Descripción

Título: Grammar-Guided Evolutionary Construction of Bayesian networks
Autor/es:
  • Font Fernández, José María
  • Manrique Gamo, Daniel
  • Pascua Salvador, Eduardo
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 4th International Conference on Interplay Between Natural and Artificial Computation, IWINAC'11
Fechas del Evento: 30/05/2011 - 03/06/2011
Lugar del Evento: Las Palmas de Gran Canaria, España
Título del Libro: Proceedings of the 4th International Conference on Interplay Between Natural and Artificial Computation, IWINAC'11
Fecha: 2011
ISBN: 978-3-642-21343-4
Materias:
Palabras Clave Informales: Evolutionary computation – Bayesian network – grammar- guided genetic programming
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

This paper proposes the EvoBANE system. EvoBANE automatically generates Bayesian networks for solving special-purpose problems. EvoBANE evolves a population of individuals that codify Bayesian networks until it finds near optimal individual that solves a given classification problem. EvoBANE has the flexibility to modify the constraints that condition the solution search space, self-adapting to the specifications of the problem to be solved. The system extends the GGEAS architecture. GGEAS is a general-purpose grammar-guided evolutionary automatic system, whose modular structure favors its application to the automatic construction of intelligent systems. EvoBANE has been applied to two classification benchmark datasets belonging to different application domains, and statistically compared with a genetic algorithm performing the same tasks. Results show that the proposed system performed better, as it manages different complexity constraints in order to find the simplest solution that best solves every problem.

Más información

ID de Registro: 12190
Identificador DC: http://oa.upm.es/12190/
Identificador OAI: oai:oa.upm.es:12190
URL Oficial: http://www.iwinac.uned.es/current/
Depositado por: Memoria Investigacion
Depositado el: 06 Sep 2012 09:22
Ultima Modificación: 21 Abr 2016 11:23
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