Evolution of control with learning classifier systems

• Main Authors: Matthew R. Karlsen, Sotiris Moschoyiannis
• Format: Article
• Language: English
• Published: SpringerOpen 2018-08-01
• Series: Applied Network Science
• Subjects: Controllability; Learning; Discovery; Boolean network; Intervention; Complex systems
• Online Access: http://link.springer.com/article/10.1007/s41109-018-0088-x

Abstract 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.