Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Incremental Learning with Respect to New Incoming Input Attributes
Neural Processing Letters
Learning with Genetic Algorithms: An Overview
Machine Learning
The Coevolution of Antibodies for Concept Learning
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Ordered incremental training for GA-based classifiers
Pattern Recognition Letters
Interference-less neural network training
Neurocomputing
Cooperative co-evolution of GA-based classifiers based on input decomposition
Engineering Applications of Artificial Intelligence
Learning concept classification rules using genetic algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Recursive hybrid decomposition with reduced pattern training
International Journal of Hybrid Intelligent Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Class decomposition for GA-based classifier agents - a Pitt approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An incremental approach to genetic-algorithms-based classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Parallel growing and training of neural networks using output parallelism
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
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
Incremental Hyperplane Partitioning for Classification
International Journal of Applied Evolutionary Computation
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Rule-based Genetic Algorithms (GAs) have been used in the application of pattern classification (Corcoran & Sen, 1994), but conventional GAs have weaknesses. First, the time spent on learning is long. Moreover, the classification accuracy achieved by a GA is not satisfactory. These drawbacks are due to existing undesirable features embedded in conventional GAs. The number of rules within the chromosome of a GA classifier is usually set and fixed before training and is not problem-dependent. Secondly, conventional approaches train the data in batch without considering whether decomposition solves the problem. Thirdly, when facing large-scale real-world problems, GAs cannot utilise resources efficiently, leading to premature convergence. Based on these observations, this paper develops a novel algorithmic framework that features automatic domain and task decomposition and problem-dependent chromosome length (rule number) selection to resolve these undesirable features. The proposed Recursive Learning of Genetic Algorithm with Task Decomposition and Varied Rule Set (RLGA) method is recursive and trains and evolves a team of learners using the concept of local fitness to decompose the original problem into sub-problems. RLGA performs better than GAs and other related solutions regarding training duration and generalization accuracy according to the experimental results.