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Learning versus optimal intervention in random Boolean networks.

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

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Abstract

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 :
NameEmailORCID
Karlsen, Matthewmatthew.r.karlsen@surrey.ac.uk
Moschoyiannis, Sotiris K.
Georgiev, Vladmirv.georgiev@surrey.ac.uk
Date : 30 December 2019
Funders : Department for Transport
DOI : https://doi.org/10.1007/s41109-019-0243-z
Grant Title : EIT Digital IVZW
Copyright Disclaimer : © The Author(s) 2019
Projects : Onward Journey Planning Association
Uncontrolled Keywords : controllability · complex networks · rule-based machine learning · XCS · optimality·intervention cost·weighted graphs
Depositing User : James Marshall
Date Deposited : 24 Jan 2020 11:25
Last Modified : 05 Feb 2020 10:08
URI: http://epubs.surrey.ac.uk/id/eprint/853387

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