Chaos synchronization of two stochastically coupled random Boolean networks

Hung, Ying; Ho, Ming‐Chung; Lih, Jiann-Shing; Jiang, I-Min

doi:10.1016/j.physleta.2006.03.025

Cited by 28 publications

(13 citation statements)

References 39 publications

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“…Examples include robot controllers ( [3], [49], and hardware implementations [38], [62]). As with the Boolean models, some work has sought to model real GRN data [31], [46] or to evolve defined global behavior, such as regular oscillations [32], [37], [40].…”

Section: Evolving Regulatory Networkmentioning

confidence: 99%

Evolving Boolean Networks on Tunable Fitness Landscapes

Bull

2012

IEEE Trans. Evol. Computat.

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This paper presents an abstract, tunable model by which to explore aspects of artificial genetic regulatory networks and their design by simulated evolution. The random Boolean network formalism is combined with the NK and NKCS models of fitness landscapes. This enables the systematic study of the interactions between the underlying genetic machinery and elements of the phenotype produced. Previously reported results from the models individually are explored within this context, using both synchronous and asynchronous updating. The evolution of network size is then explored in particular under varying conditions.

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Section: Evolving Regulatory Networkmentioning

confidence: 99%

Evolving Boolean Networks on Tunable Fitness Landscapes

Bull

2012

IEEE Trans. Evol. Computat.

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“…A very small number of studies have considered multiple, coupled RBN (e.g., [Hung et al, 2006] [Bull & Alonso-Sanz, 2008]) but no previous consideration of ensemble scenarios, as here, are known.…”

Section: Random Boolean Networkmentioning

confidence: 99%

On dynamical genetic programming: simple Boolean networks in learning classifier systems

Bull

2009

International Journal of Parallel, Emergent and Distributed Sys

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Abstract. Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end.Significantly, Turing proposed a form of discrete dynamical system and yet dynamical representations remain almost unexplored within conventional genetic programming. This paper presents results from an initial investigation into using simple dynamical genetic programming representations within a Learning Classifier System. It is shown possible to evolve ensembles of dynamical Boolean function networks to solve versions of the well-known multiplexer problem. Both synchronous and asynchronous systems are considered.

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“…The topic of synchronization of Boolean networks is also of interest especially in areas such as neural networks [23,24,33,34]. An analysis of deterministic or stochastic synchronization of networks governed by the generalized ECA Rule 22 of this paper could yield interesting results.…”

Section: Discussionmentioning

confidence: 95%

“…If the network has 3 nodes and d 1 = d 2 = 1 3 we obtain the ECA Rule 22. Hung et al [24] have used a simpler generalization of ECA Rule 22 in the context of synchronization of stochastically coupled random Boolean networks: a node is turned ON if and only if a single parent is ON. The new generalization provided in (2), allows us to analyze a wider variety of totalistic rules and provides an insight into Boolean systems governed by rules meaningful for neural networks or biology.…”

Section: The Boolean Networkmentioning

confidence: 99%