Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions

Muelas Pascual, Santiago and Peña Sanchez, Jose Maria and LaTorre de la Fuente, Antonio (2010). Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions. In: "EvoApplicatons 2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC", 07/04/2010 - 09/04/2010, Estambul, Turquía. ISBN 978-3-642-12238-5.

Description

Title: Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions
Author/s:
  • Muelas Pascual, Santiago
  • Peña Sanchez, Jose Maria
  • LaTorre de la Fuente, Antonio
Item Type: Presentation at Congress or Conference (Article)
Event Title: EvoApplicatons 2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC
Event Dates: 07/04/2010 - 09/04/2010
Event Location: Estambul, Turquía
Title of Book: Proceedings of the EvoApplicatons 2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC
Date: 2010
ISBN: 978-3-642-12238-5
Volume: 6024
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

ection of the most appropriate Evolutionary Algorithm for a given optimization problem is a difficult task. Hybrid Evolutionary Algorithms are a promising alternative to deal with this problem. By means of the combination of different heuristic optimization approaches, it is possible to profit from the benefits of the best approach, avoiding the limitations of the others. Nowadays, there is an active research in the design of dynamic or adaptive hybrid algorithms. However, little research has been done in the automatic learning of the best hybridization strategy. This paper proposes a mechanism to learn a strategy based on the analysis of the results from past executions. The proposed algorithm has been evaluated on a well-known benchmark on continuous optimization. The obtained results suggest that the proposed approach is able to learn very promising hybridization strategies.

More information

Item ID: 7687
DC Identifier: http://oa.upm.es/7687/
OAI Identifier: oai:oa.upm.es:7687
Official URL: http://www.springerlink.com/content/x370062853393740/
Deposited by: Memoria Investigacion
Deposited on: 29 Jun 2011 08:20
Last Modified: 20 Apr 2016 16:45
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