Grammar-Guided Evolutionary Construction of Bayesian networks

Font Fernández, José María and Manrique Gamo, Daniel and Pascua Salvador, Eduardo (2011). Grammar-Guided Evolutionary Construction of Bayesian networks. In: "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.

Description

Title: Grammar-Guided Evolutionary Construction of Bayesian networks
Author/s:
  • Font Fernández, José María
  • Manrique Gamo, Daniel
  • Pascua Salvador, Eduardo
Item Type: Presentation at Congress or Conference (Article)
Event Title: 4th International Conference on Interplay Between Natural and Artificial Computation, IWINAC'11
Event Dates: 30/05/2011 - 03/06/2011
Event Location: Las Palmas de Gran Canaria, España
Title of Book: Proceedings of the 4th International Conference on Interplay Between Natural and Artificial Computation, IWINAC'11
Date: 2011
ISBN: 978-3-642-21343-4
Subjects:
Freetext Keywords: Evolutionary computation – Bayesian network – grammar- guided genetic programming
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 12190
DC Identifier: http://oa.upm.es/12190/
OAI Identifier: oai:oa.upm.es:12190
Official URL: http://www.iwinac.uned.es/current/
Deposited by: Memoria Investigacion
Deposited on: 06 Sep 2012 09:22
Last Modified: 21 Apr 2016 11:23
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