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

Bull, L. (2001) Simple Markov models of the genetic algorithm in classifier systems: multi-step tasks. Lecture Notes in Computer Science, 1996. pp. 29-36. ISSN 0302-9743 Available from: http://eprints.uwe.ac.uk/20747

Full text not available from this repository

Publisher's URL: http://www.springerlink.com

Abstract/Description

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...

Item Type:Article
Uncontrolled Keywords:Markov, models, genetic algorithm, classifier systems, multi step tasks
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:20747
Deposited By: M. Clarke
Deposited On:12 Jul 2013 08:33
Last Modified:15 Nov 2016 22:21

Request a change to this item