Self-adaptive mutation in ZCS controllers

Bull, Larry; Hurst, Jacob

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.

• Presentation Conference Type: Conference Paper (published)
• 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
• Publisher: Springer Verlag
• Peer Reviewed: Not Peer Reviewed
• Volume: 1803
• Pages: 339-346
• Series Title: Lecture Notes in Computer Science
• Series Number: 1803
• 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