Publication
Our paper, titled Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems, has been published in IEEE Transactions on Evolutionary Computation (TEVC) 2025.
This work introduces a novel approach to rule representation in Learning Classifier Systems (LCS) using four-parameter beta distribution, significantly improving the adaptability and performance of evolutionary learning systems.
Research Highlights
- Novel four-parameter beta distribution for rule representation
- Enhanced adaptability in Learning Classifier Systems
- Improved performance across various benchmark problems
- Theoretical analysis and empirical validation
Impact
This research advances the field of evolutionary computation by providing more flexible and effective rule representation mechanisms, with applications in pattern recognition, data mining, and adaptive systems.
Authors: Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama, Keiki Takadama, Hisao Ishibuchi, and Masaya Nakata
Venue: IEEE Transactions on Evolutionary Computation (TEVC) 2025