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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1107

Title: An incremental approach to genetic algorithms based classification
Authors: Guan, SU
Zhu, F
Keywords: Incremental learning
Genetic algorithms
Classifier agents
Publication Date: 2005
Publisher: IEEE
Citation: IEEE Transactions on Systems, Man and Cybernetics Part B, 35(2): 227 - 239, Apr 2005
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 multi-agent 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.
URI: http://bura.brunel.ac.uk/handle/2438/1107
ISSN: 1083-4419
Appears in Collections:School of Engineering and Design Research papers
Electronic and Computer Engineering

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