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

Bull, L. and Preen, R. (2009) On dynamical genetic programming: Random boolean networks in learning classifier systems. In: 12th European Conference on Genetic Programming, EuroGP 2009 Tübingen, April 15-17 2009, Tübingen, Germany. Germany: UNSPECIFIED, pp. 37-48 Available from: http://eprints.uwe.ac.uk/22397

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Publisher's URL: http://dx.doi.org/10.1007/978-3-642-01181-8_4

Abstract/Description

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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:learning classifier systems, random boolean networks
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:22397
Deposited By: Dr R. Preen
Deposited On:10 Feb 2014 13:59
Last Modified:15 Nov 2016 22:20

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