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GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
GECCO
2009
Springer
122views Optimization» more  GECCO 2009»
14 years 2 months ago
Evolving symmetric and modular neural networks for distributed control
Problems such as the design of distributed controllers are characterized by modularity and symmetry. However, the symmetries useful for solving them are often difficult to determ...
Vinod K. Valsalam, Risto Miikkulainen
IWSOS
2011
Springer
12 years 10 months ago
Evolving Self-organizing Cellular Automata Based on Neural Network Genotypes
Abstract This paper depicts and evaluates an evolutionary design process for generating a complex self-organizing multicellular system based on Cellular Automata (CA). We extend th...
Wilfried Elmenreich, István Fehérv&a...
ESANN
2006
13 years 9 months ago
Optimal design of hierarchical wavelet networks for time-series forecasting
The purpose of this study is to identify the Hierarchical Wavelet Neural Networks (HWNN) and select important input features for each sub-wavelet neural network automatically. Base...
Yuehui Chen, Bo Yang, Ajith Abraham
IJCNN
2007
IEEE
14 years 2 months ago
Character Recognition using Spiking Neural Networks
— A spiking neural network model is used to identify characters in a character set. The network is a two layered structure consisting of integrate-and-fire and active dendrite n...
Ankur Gupta, Lyle N. Long