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Optimal control rules for random Boolean networks

Karlsen, Matthew R. and Moschoyiannis, Sotiris K. (2018) Optimal control rules for random Boolean networks In: The 7th International Conference on Complex Networks and Their Applications (Complex Networks 2018), 11-13 Dec 2018, Cambridge, United Kingdom.

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A random Boolean network (RBN) may be controlled through the use of a learning classifier system (LCS) – an eXtended Classifier System (XCS) can evolve a rule set that directs an RBN from any state to a target state. However, the rules evolved may not be optimal, in terms of minimising the total cost of the paths used to direct the network from any state to a specified attractor. Here we uncover the optimal set of control rules via an exhaustive algorithm. The performance of an LCS (XCS) on the RBN control problem is assessed in light of the newly uncovered optimal rule set.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Karlsen, Matthew
Moschoyiannis, Sotiris
Editors :
Aiello, L
Cherifi, C
Cherifi, H
Lambiotte, R
Lió, P
Rocha, L
Date : 2 December 2018
DOI : 10.1007/978-3-030-05411-3_66
Copyright Disclaimer : © The authors 2018. This is a post-peer-review, pre-copyedit version of an article published in Complex Networks and their Applications: Proceedings of Complex Networks 2018. The final authenticated version is available online at:
Related URLs :
Additional Information : Part of the 'Lecture Notes in Computer Science' series
Depositing User : Clive Harris
Date Deposited : 11 Oct 2018 01:53
Last Modified : 12 Dec 2018 15:51

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