An evolutionary memetic algorithm for rule extraction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1302-1315
Journal / PublicationExpert Systems with Applications
Volume37
Issue number2
Publication statusPublished - Mar 2010
Externally publishedYes

Abstract

In this paper, an Evolutionary Memetic Algorithm (EMA), which uses a local search intensity scheme to complement the global search capability of Evolutionary Algorithms (EAs), is proposed for rule extraction. Two schemes for local search are studied, namely EMA-μGA, which uses a micro-Genetic Algorithm-based (μGA) technique, and EMA-AIS, which is inspired by Artificial Immune System (AIS) and uses the clonal selection for cell proliferation. The evolutionary memetic algorithm is complemented with the use of a variable-length chromosome structure, which allows the flexibility to model the number of rules required. In addition, advanced variation operators are used to improve different aspects of the algorithm. Real world benchmarking problems are used to validate the performance of EMA and results from simulations show the proposed algorithm is effective. © 2009 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Artificial immune systems, Evolutionary Algorithms, Memetic search, Rule extraction

Citation Format(s)

An evolutionary memetic algorithm for rule extraction. / Ang, J. H.; Tan, K. C.; Mamun, A. A.

In: Expert Systems with Applications, Vol. 37, No. 2, 03.2010, p. 1302-1315.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review