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Learning versus optimal intervention in random Boolean networks

Karlsen, Matthew R., Moschoyiannis, Sotiris K. and Georgiev, Vlad B. (2019) Learning versus optimal intervention in random Boolean networks Applied Network Science, 4 (129).

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Random Boolean Networks (RBNs) are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine learning, specifically through the use of a learning classifier system (LCS) – an eXtended Classifier System (XCS) can evolve a set of condition-action rules that direct an RBN from any state to a target state (attractor). However, the rules evolved by XCS may not be optimal, in terms of minimising the total cost along the paths used to direct the network from any state to a specified attractor. In this paper, we present an algorithm for uncovering the optimal set of control rules for controlling random Boolean networks. We assign relative costs for interventions and ‘natural’ steps. We then compare the performance of this optimal rule calculator algorithm (ORC) and the XCS variant of learning classifier systems. We find that the rules evolved by XCS are not optimal in terms of total cost. The results provide a benchmark for future improvement.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Karlsen, Matthew R.
Moschoyiannis, Sotiris
Georgiev, Vlad
Date : 30 December 2019
Funders : Department for Transport - Innovate UK, EPSRC - Engineering and Physical Sciences Research Council
DOI : 10.1007/s41109-019-0243-z
Copyright Disclaimer : © The author(s). 2019 Open Access 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; Complex networks; Rule-based machine learning; XCS; Optimality; Intervention cost; Weighted graphs
Depositing User : Diane Maxfield
Date Deposited : 30 Jan 2020 12:08
Last Modified : 30 Jan 2020 12:08

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