Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.eswa.2009.06.028
Title: An evolutionary memetic algorithm for rule extraction
Authors: Ang, J.H.
Tan, K.C. 
Mamun, A.A. 
Keywords: Artificial immune systems
Evolutionary Algorithms
Memetic search
Rule extraction
Issue Date: Mar-2010
Source: Ang, J.H., Tan, K.C., Mamun, A.A. (2010-03). An evolutionary memetic algorithm for rule extraction. Expert Systems with Applications 37 (2) : 1302-1315. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eswa.2009.06.028
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.
Source Title: Expert Systems with Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/55015
ISSN: 09574174
DOI: 10.1016/j.eswa.2009.06.028
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