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ICGA
1993
145views Optimization» more  ICGA 1993»
13 years 6 months ago
Genetic Programming of Minimal Neural Nets Using Occam's Razor
A genetic programming method is investigated for optimizing both the architecture and the connection weights of multilayer feedforward neural networks. The genotype of each networ...
Byoung-Tak Zhang, Heinz Mühlenbein
IJCNN
2008
IEEE
13 years 11 months ago
Biologically realizable reward-modulated hebbian training for spiking neural networks
— Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximat...
Silvia Ferrari, Bhavesh Mehta, Gianluca Di Muro, A...
ASC
2004
13 years 5 months ago
Extracting rules from trained neural network using GA for managing E-business
Theabilitytointelligentlycollect,manageandanalyzeinformationaboutcustomersandsellersisakeysourceofcompetitive advantage for an e-business. This ability provides an opportunity to ...
Atta Ebrahim E. ElAlfi, R. Haque, M. Esmel ElAlami
NIPS
1994
13 years 6 months ago
Boosting the Performance of RBF Networks with Dynamic Decay Adjustment
Radial Basis Function (RBF) Networks, also known as networks of locally{tuned processing units (see 6]) are well known for their ease of use. Most algorithms used to train these t...
Michael R. Berthold, Jay Diamond
GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
13 years 10 months ago
A pareto archive evolutionary strategy based radial basis function neural network training algorithm for failure rate prediction
This paper outlines a radial basis function neural network approach to predict the failures in overhead distribution lines of power delivery systems. The RBF networks are trained ...
Grant Cochenour, Jerad Simon, Sanjoy Das, Anil Pah...