Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2004.842247
Title: An incremental approach to genetic-algorithms-based classification
Authors: Guan, S.-U. 
Zhu, F.
Keywords: Classifier agents
Genetic algorithms (GAs)
Incremental learning
Issue Date: Apr-2005
Source: Guan, S.-U., Zhu, F. (2005-04). An incremental approach to genetic-algorithms-based classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35 (2) : 227-239. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2004.842247
Abstract: Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multiagent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an "integration" operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed. © 2005 IEEE.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/55031
ISSN: 10834419
DOI: 10.1109/TSMCB.2004.842247
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