On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems

2009 ◽  
pp. 37-48 ◽  
Author(s):  
Larry Bull ◽  
Richard Preen
Keyword(s):  
Genetic Programming ◽  
Boolean Networks ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Random Boolean Networks ◽  
Learning Classifier

scholarly journals Learning versus optimal intervention in random Boolean networks

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Matthew R. Karlsen ◽  
Sotiris K. Moschoyiannis ◽  
Vlad B. Georgiev
Keyword(s):  
Boolean Networks ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
Complex Behaviour ◽  
Total Cost ◽  
Random Boolean Networks ◽  
Classifier System ◽  
Control Rules ◽  
Learning Classifier ◽  
Optimal Intervention

AbstractRandom 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.

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