On dynamical genetic programming: Random boolean networks in learning classifier systems

Bull, Larry; Preen, Richard

Abstract

Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end. Significantly, Turing proposed a form of discrete dynamical system and yet dynamical representations remain almost unexplored within genetic programming. This paper presents results from an initial investigation into using a simple dynamical genetic programming representation within a Learning Classifier System. It is shown possible to evolve ensembles of dynamical Boolean function networks to solve versions of the well-known multiplexer problem. Both synchronous and asynchronous systems are considered.© Springer-Verlag Berlin Heidelberg 2009.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 12th European Conference on Genetic Programming, EuroGP 2009 Tübingen, Germany, April 15-17, 2009
• Publication Date: Jul 23, 2009
• 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: Peer Reviewed
• Volume: 5481 LNCS
• Pages: 37-48
• Book Title: Genetic Programming
• DOI: https://doi.org/10.1007/978-3-642-01181-8_4
• Keywords: learning classifier systems, random boolean networks
• Public URL: https://uwe-repository.worktribe.com/output/996818
• Publisher URL: http://dx.doi.org/10.1007/978-3-642-01181-8_4
• Additional Information: Title of Conference or Conference Proceedings : 12th European Conference on Genetic Programming, EuroGP 2009 Tübingen