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GECCO
2003
Springer
153views Optimization» more  GECCO 2003»
14 years 20 days ago
SEPA: Structure Evolution and Parameter Adaptation in Feed-Forward Neural Networks
Abstract. In developing algorithms that dynamically changes the structure and weights of ANN (Artificial Neural Networks), there must be a proper balance between network complexit...
Paulito P. Palmes, Taichi Hayasaka, Shiro Usui
ERSA
2006
91views Hardware» more  ERSA 2006»
13 years 8 months ago
Intrinsic Embedded Hardware Evolution of Block-based Neural Networks
- An intrinsic embedded online evolution system has been designed using Block-based neural networks and implemented on Xilinx VirtexIIPro FPGAs. The designed network can dynamicall...
Saumil Merchant, Gregory D. Peterson, Seong Kong
GECCO
2007
Springer
172views Optimization» more  GECCO 2007»
14 years 1 months ago
Acquiring evolvability through adaptive representations
Adaptive representations allow evolution to explore the space of phenotypes by choosing the most suitable set of genotypic parameters. Although such an approach is believed to be ...
Joseph Reisinger, Risto Miikkulainen
CEC
2009
IEEE
14 years 2 months ago
Evolving modular neural-networks through exaptation
— Despite their success as optimization methods, evolutionary algorithms face many difficulties to design artifacts with complex structures. According to paleontologists, living...
Jean-Baptiste Mouret, Stéphane Doncieux
IJCNN
2007
IEEE
14 years 1 months ago
Dynamic Pooling for the Combination of Forecasts generated using Multi Level Learning
— In this paper we provide experimental results and extensions to our previous theoretical findings concerning the combination of forecasts that have been diversified by three ...
Silvia Riedel, Bogdan Gabrys