Fuzzy dynamical genetic programming in XCSF

Preen, Richard; Bull, Larry

Abstract

A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Genetic Programming (DGP). This paper presents results from an investigation into using a fuzzy DGP representation within the XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic Networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such fuzzy dynamical systems within XCSF to solve several well-known continuous-valued test problems. © 2011 Authors.

Citation

Preen, R., & Bull, L. (2011). Fuzzy dynamical genetic programming in XCSF. In N. Krasnogor, & P. L. Lanzi (Eds.), Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (167-168). https://doi.org/10.1145/2001858.2001952

• Conference Name: Genetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication
• Conference Location: Dublin, Ireland
• Start Date: Jul 12, 2019
• End Date: Jul 16, 2019
• Publication Date: Aug 26, 2011
• Peer Reviewed: Peer Reviewed
• Pages: 167-168
• Book Title: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
• ISBN: 9781450306904
• DOI: https://doi.org/10.1145/2001858.2001952
• Keywords: fuzzy logic networks, learning classifier systems, reinforcement learning, self-adaptation, XCSF
• Public URL: https://uwe-repository.worktribe.com/output/961378
• Publisher URL: http://dx.doi.org/10.1145/2001858.2001952
• Additional Information: Title of Conference or Conference Proceedings : 13th annual conference on genetic and evolutionary computation (GECCO '11)