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Knowledge-Based Fast Evaluation for Evolutionary Learning

Opened Access Knowledge-Based Fast Evaluation for Evolutionary Learning

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Autor: Giráldez Rojo, Raúl
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2005
Publicado en: IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35 (2), 254-261.
Tipo de documento: Artículo
Resumen: The increasing amount of information available is encouraging the search for efficient techniques to improve the data mining methods, especially those which consume great computational resources, such as evolutionary computation.Efficacy and efficiency are two critical aspects for knowledge-based techniques.The incorporation of knowledge into evolutionary algorithms (EAs) should provide either better solutions (efficacy) or the equivalent solutions in shorter time (efficiency), regarding the same evolutionary algorithm without incorporating such knowledge. In this paper, we categorize and summarize some of the incorporation of knowledge techniques for evolutionary algorithms and present a novel data structure, called efficient evaluation structure (EES), which helps the evolutionary algorithm to provide decision rules using less computational resources.The EES-based EA is tested and compared to another EA system and the experimental results show the quality of our approac...
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Cita: Giráldez Rojo, R., Aguilar Ruiz, J.S. y Riquelme Santos, J.C. (2005). Knowledge-Based Fast Evaluation for Evolutionary Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35 (2), 254-261.
Tamaño: 598.4Kb
Formato: PDF

URI: http://hdl.handle.net/11441/43224

DOI: http://dx.doi.org/10.1109/TSMCC.2004.841904

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