Use of a connection-selection scheme in neural XCSF

Howard, Gerard David; Bull, Larry; Lanzi, Pier Luca

Abstract

XCSF is a modern form of Learning Classifier System (LCS) that has proven successful in a number of problem domains. In this paper we exploit the modular nature of XCSF to include a number of extensions, namely a neural classifier representation, self-adaptive mutation rates and neural constructivism. It is shown that, via constructivism, appropriate internal rule complexity emerges during learning. It is also shown that self-adaptation allows this rule complexity to emerge at a rate controlled by the learner. We evaluate this system on both discrete and continuous-valued maze environments. The main contribution of this work is the implementation of a feature selection derivative (termed connection selection), which is applied to modify network connectivity patterns. We evaluate the effect of connection selection, in terms of both solution size and system performance, on both discrete and continuous-valued environments. © 2010 Springer-Verlag Berlin Heidelberg.

Citation

Howard, G. D., Bull, L., & Lanzi, P. L. (2010). Use of a connection-selection scheme in neural XCSF. Lecture Notes in Artificial Intelligence, 6471 LNAI, 87-106. https://doi.org/10.1007/978-3-642-17508-4_7

• Journal Article Type: Conference Paper
• Publication Date: Dec 1, 2010
• 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: Peer Reviewed
• Volume: 6471 LNAI
• Pages: 87-106
• Series Title: Lecture Notes in Computer Science
• Series Number: 6471
• DOI: https://doi.org/10.1007/978-3-642-17508-4_7
• Keywords: computation by abstract devices, computer science, artificial intelligence, robotics, algorithm analysis, problem complexity, database management, information systems applications, internet
• Public URL: https://uwe-repository.worktribe.com/output/985707
• Publisher URL: http://www.springerlink.com