International Journal of Hybrid Intelligent Systems
Enhanced Cooperative Co-evolution Genetic Algorithm for Rule-Based Pattern Classification
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
IEEE Transactions on Evolutionary Computation
A parallel genetic programming algorithm for classification
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Mining data streams with concept drifts using genetic algorithm
Artificial Intelligence Review
Recursive and incremental learning GA featuring problem-dependent rule-set
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
A new method of mining data streams using harmony search
Journal of Intelligent Information Systems
Recursive Learning of Genetic Algorithms with Task Decomposition and Varied Rule Set
International Journal of Applied Evolutionary Computation
An interpretable classification rule mining algorithm
Information Sciences: an International Journal
Parallel multi-objective Ant Programming for classification using GPUs
Journal of Parallel and Distributed Computing
Information Sciences: an International Journal
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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.