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Self-adaptive mutation in ZCS controllers

Bull, Larry; Hurst, Jacob

Authors

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor

Jacob Hurst



Contributors

S. Cagnoni
Editor

R. Poli
Editor

G. D. Smith
Editor

D. Corne
Editor

M. Oates
Editor

E. Hart
Editor

P. L. Lanzi
Editor

E. J. Willem
Editor

Y. Li
Editor

B. Paechter
Editor

T. C. Fogarty
Editor

Abstract

© Springer-Verlag Berlin Heidelberg 2000. The use and benefits of self-adaptive mutation operators are well-known within evolutionary computing. In this paper we examine the use of self-adaptive mutation in Michigan-style Classifier Systems with the aim of improving their performance as controllers for autonomous mobile robots. Initially, we implement the operator in the ZCS classifier and examine its performance in two "animat" environments. It is shown that, although no significant increase in performance is seen over results presented in the literature using a fixed rate of mutation, the operator adapts to approximately this rate regardless of the initial range.

Citation

Bull, L., & Hurst, J. (2000). Self-adaptive mutation in ZCS controllers. Lecture Notes in Artificial Intelligence, 1803, 339-346. https://doi.org/10.1007/3-540-45561-2_33

Journal Article Type Conference Paper
Publication Date Jan 1, 2000
Journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer Verlag
Peer Reviewed Not Peer Reviewed
Volume 1803
Pages 339-346
Series Title Lecture Notes in Computer Science
Series Number 1803
ISBN ;
DOI https://doi.org/10.1007/3-540-45561-2_33
Keywords algorithm analysis and problem complexity, computer communication networks, image processing and computer vision, systems and information theory in engineering
Public URL https://uwe-repository.worktribe.com/output/1094889
Publisher URL http://dx.doi.org/10.1007/3-540-45561-2_33