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Evolution of control with learning classifier systems

Karlsen, Matthew and Moschoyiannis, Sotiris (2018) Evolution of control with learning classifier systems Applied Network Science, 3 (30). pp. 1-36.

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In this paper we describe the application of a learning classifier system (LCS) variant known as the eXtended classifier system (XCS) to evolve a set of ‘control rules’ for a number of Boolean network instances. We show that (1) it is possible to take the system to an attractor, from any given state, by applying a set of ‘control rules’ consisting of ternary conditions strings (i.e. each condition component in the rule has three possible states; 0, 1 or #) with associated bit-flip actions, and (2) that it is possible to discover such rules using an evolutionary approach via the application of a learning classifier system. The proposed approach builds on learning (reinforcement learning) and discovery (a genetic algorithm) and therefore the series of interventions for controlling the network are determined but are not fixed. System control rules evolve in such a way that they mirror both the structure and dynamics of the system, without having ‘direct’ access to either.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Date : 13 August 2018
DOI : 10.1007/s41109-018-0088-x
Copyright Disclaimer : © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Uncontrolled Keywords : Controllability, Learning, Discovery, Boolean network, Intervention, Complex systems, LCS, XCS
Depositing User : Melanie Hughes
Date Deposited : 14 Aug 2018 14:01
Last Modified : 18 Sep 2018 13:11

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