Recursive and incremental learning GA featuring problem-dependent rule-set

Authors:
Lei Fang
Affiliations:
School of Computer Science, University of St Andrews, St Andrews, UK
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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Abstract

Traditional rule-based classifiers training with Genetic Algorithms have their major weaknesses in the classification accuracy and training time. To resolve these drawbacks, this paper reviews Recursive Learning of Genetic Algorithm with Task Decomposition and Varied Rule Set (RLGA) and proposes its variation that features Incremental Attribute Learning (RLGA-I). Experiments show that both the proposed solutions dramatically reduce the training duration with better generalization accuracy.