Parallel island-based genetic algorithm for radio network design
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Incremental Learning with Respect to New Incoming Input Attributes
Neural Processing Letters
Learning Classifier Systems, From Foundations to Applications
Learning Classifier Systems, From Foundations to Applications
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Ordered incremental training with genetic algorithms
International Journal of Intelligent Systems
Ordered incremental training for GA-based classifiers
Pattern Recognition Letters
Class decomposition for GA-based classifier agents - a Pitt approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental learning methods with retrieving of interfered patterns
IEEE Transactions on Neural Networks
Recursive Learning of Genetic Algorithms with Task Decomposition and Varied Rule Set
International Journal of Applied Evolutionary Computation
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The authors propose an incremental hyperplane partitioning approach to classification. Hyperplanes that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Genetic Algorithm GA. A new method-Incremental Linear Encoding based Genetic Algorithm ILEGA is proposed to tackle the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. The authors solve classification problems through a simple and flexible chromosome encoding scheme, where the partitioning rules are encoded by linear equations rather than If-Then rules. Moreover, an incremental approach combined with output portioning and pattern reduction is applied to cope with the curse of dimensionality. The algorithm is tested with six datasets. The experimental results show that ILEGA outperform in both lower-and higher-dimensional problems compared with the original GA.