Simple Markov models of the genetic algorithm in classifier systems: multi-step tasks

Bull, Larry

Abstract

Abstract Michigan-style Classifier Systems use Genetic Algorithms to facilitate rule discovery. This paper presents a simple Markov model of the algorithm in such systems, with the aim of examining the effects of different types of interdependence between niches in multi-step tasks. Using the model it is shown that the existence of, what is here termed, partner rule variance can have significant and detrimental effects on the Genetic Algorithm's expected behaviour. Suggestions are made as to how to reduce these effects, making...

Citation

Bull, L. (2001). Simple Markov models of the genetic algorithm in classifier systems: multi-step tasks. Lecture Notes in Artificial Intelligence, 1996, 29-36

• Journal Article Type: Article
• Publication Date: Jan 1, 2001
• Journal: Lecture Notes in Computer Science
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 1996
• Pages: 29-36
• Keywords: Markov, models, genetic algorithm, classifier systems, multi step tasks
• Public URL: https://uwe-repository.worktribe.com/output/1090721
• Publisher URL: http://www.springerlink.com