Controlling complex dynamics with artificial biochemical networks

Authors:
Michael A. Lones;Andy M. Tyrrell;Susan Stepney;Leo S. Caves
Affiliations:
Department of Electronics, University of York, Heslington, York, UK;Department of Electronics, University of York, Heslington, York, UK;Department of Computer Science, University of York, Heslington, York, UK;Department of Biology, University of York, Heslington, York, UK
Venue:
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Year:
2010

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Abstract

Artificial biochemical networks (ABNs) are computational models inspired by the biochemical networks which underlie the cellular activities of biological organisms. This paper shows how evolved ABNs may be used to control chaotic dynamics in both discrete and continuous dynamical systems, illustrating that ABNs can be used to represent complex computational behaviours within evolutionary algorithms. Our results also show that performance is sensitive to model choice, and suggest that conservation laws play an important role in guiding search.