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CORR
2002
Springer
97views Education» more  CORR 2002»
10 years 4 months ago
Classification of Random Boolean Networks
We provide the first classification of different types of Random Boolean Networks (RBNs). We study the differences of RBNs depending on the degree of synchronicity and determinism...
Carlos Gershenson
GECCO
2008
Springer
179views Optimization» more  GECCO 2008»
10 years 5 months ago
Evolution of discrete gene regulatory models
Gene regulatory networks (GRNs) are complex control systems that govern the interaction of genes, which ultimately control cellular processes at the protein level. GRNs can be ted...
Afshin Esmaeili, Christian Jacob
CEC
2003
IEEE
10 years 8 months ago
Dynamics of gene expression in an artificial genome
Abstract- Complex systems techniques provide a powerful tool to study the emergent properties of networks of interacting genes. In this study we extract models of genetic regulator...
Kai Willadsen, Janet Wiles
GECCO
2009
Springer
122views Optimization» more  GECCO 2009»
10 years 8 months ago
Visualising random boolean network dynamics
We propose a simple approach to visualising the time behaviour of Random Boolean Networks (RBNs), and demonstrate the approach by examining the effect of canalising functions for ...
Susan Stepney
ECAL
2003
Springer
10 years 9 months ago
Contextual Random Boolean Networks
Abstract. We propose the use of Deterministic Generalized Asynchronous Random Boolean Networks [1] as models of contextual deterministic discrete dynamical systems. We show that ch...
Carlos Gershenson, Jan Broekaert, Diederik Aerts
AHS
2006
IEEE
125views Hardware» more  AHS 2006»
10 years 10 months ago
Evolving Hardware with Self-reconfigurable connectivity in Xilinx FPGAs
Randomly connecting networks have proven to be universal computing machines. By interconnecting a set of nodes in a random way one can model very complicated non-linear dynamic sy...
Andres Upegui, Eduardo Sanchez
GECCO
2009
Springer
150views Optimization» more  GECCO 2009»
10 years 11 months ago
Discrete dynamical genetic programming in XCS
A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results fr...
Richard Preen, Larry Bull
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