Evolving Multi-valued Regulatory Networks on Tunable Fitness Landscapes
Larry Bull
Department of Computer Science and Creative Technologies
University of the West of England
Frenchay
Bristol, BS16 1QY, UK
Abstract
Random Boolean networks have been used widely to explore aspects of gene regulatory networks. As the name implies, traditionally the model has used a binary representation scheme. This paper uses a modified form of the model to systematically explore the effects of increasing the number of gene states. These random multi-valued networks are evolved within rugged fitness landscapes to explore their behavior. Results suggest the basic properties of the original model remain, regardless of the update scheme or fitness sampling method. Changes are seen in sensitivity to high levels of connectivity, the mutation rate and the ability to vary network size.
Keywords: asynchronous; growth; mutation; NK model
Cite this publication as:
L. Bull, “Evolving Multi-valued Regulatory Networks on Tunable Fitness Landscapes,” Complex Systems, 32(3), 2023 pp. 289–307.
https://doi.org/10.25088/ComplexSystems.32.3.289
