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Evolution of control with learning classifier systems

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...

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Detalles Bibliográficos
Autores principales: Karlsen, Matthew R., Moschoyiannis, Sotiris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214302/
https://www.ncbi.nlm.nih.gov/pubmed/30839802
http://dx.doi.org/10.1007/s41109-018-0088-x
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author Karlsen, Matthew R.
Moschoyiannis, Sotiris
author_facet Karlsen, Matthew R.
Moschoyiannis, Sotiris
author_sort Karlsen, Matthew R.
collection PubMed
description 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.
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spelling pubmed-62143022018-11-13 Evolution of control with learning classifier systems Karlsen, Matthew R. Moschoyiannis, Sotiris Appl Netw Sci Research 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. Springer International Publishing 2018-08-13 2018 /pmc/articles/PMC6214302/ /pubmed/30839802 http://dx.doi.org/10.1007/s41109-018-0088-x Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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.
spellingShingle Research
Karlsen, Matthew R.
Moschoyiannis, Sotiris
Evolution of control with learning classifier systems
title Evolution of control with learning classifier systems
title_full Evolution of control with learning classifier systems
title_fullStr Evolution of control with learning classifier systems
title_full_unstemmed Evolution of control with learning classifier systems
title_short Evolution of control with learning classifier systems
title_sort evolution of control with learning classifier systems
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214302/
https://www.ncbi.nlm.nih.gov/pubmed/30839802
http://dx.doi.org/10.1007/s41109-018-0088-x
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