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GPEM
2006
82views more  GPEM 2006»
13 years 6 months ago
Shortcomings with using edge encodings to represent graph structures
There are various representations for encoding graph structures, such as artificial neural networks (ANNs) and circuits, each with its own strengths and weaknesses. Here we analyz...
Gregory Hornby
GLVLSI
2003
IEEE
175views VLSI» more  GLVLSI 2003»
13 years 12 months ago
A custom FPGA for the simulation of gene regulatory networks
We present a unique FPGA that uses a mix of digital and large-signal analog computation for the simulation of gene regulatory networks. The prototype IC consists of a 4x5 array of...
Ilias Tagkopoulos, Charles A. Zukowski, German Cav...
IJCNN
2000
IEEE
13 years 11 months ago
Evolving Neural Network Structures Using Axonal Growth Mechanisms
In the eld of arti cial evolution creating methods to evolve neural networks is an important goal. But how to encode the structure and properties of the neural network in the geno...
Peter Eggenberger
GECCO
2009
Springer
150views Optimization» more  GECCO 2009»
14 years 1 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
GECCO
2011
Springer
274views Optimization» more  GECCO 2011»
12 years 10 months ago
Fuzzy dynamical genetic programming in XCSF
—A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical ...
Richard Preen, Larry Bull