Please use this identifier to cite or link to this item: https://doi.org/10.1002/int.10145
Title: Incremental learning of collaborative classifier agents with new class acquisition: An incremental genetic algorithm approach
Authors: Guan, S.-U. 
Zhu, F.
Issue Date: Nov-2003
Source: Guan, S.-U., Zhu, F. (2003-11). Incremental learning of collaborative classifier agents with new class acquisition: An incremental genetic algorithm approach. International Journal of Intelligent Systems 18 (11) : 1173-1192. ScholarBank@NUS Repository. https://doi.org/10.1002/int.10145
Abstract: A number of soft computing approaches such as neural networks, evolutionary algorithms, and fuzzy logic have been widely used for classifier agents to adaptively evolve solutions on classification problems. However, most work in the literature focuses on the learning ability of the individual classifier agent. This article explores incremental, collaborative learning in a multiagent environment. We use the genetic algorithm (GA) and incremental GA (IGA) as the main techniques to evolve the rule set for classification and apply new class acquisition as a typical example to illustrate the incremental, collaborative learning capability of classifier agents. Benchmark data sets are used to evaluate proposed approaches. The results show that GA and IGA can be used successfully for collaborative learning among classifier agents.
Source Title: International Journal of Intelligent Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/56311
ISSN: 08848173
DOI: 10.1002/int.10145
Appears in Collections:Staff Publications

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