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UAI
2008
15 years 2 months ago
The Computational Complexity of Sensitivity Analysis and Parameter Tuning
While known algorithms for sensitivity analysis and parameter tuning in probabilistic networks have a running time that is exponential in the size of the network, the exact comput...
Johan Kwisthout, Linda C. van der Gaag
CEC
2010
IEEE
15 years 2 months ago
An analysis of massively distributed evolutionary algorithms
Computational science is placing new demands on optimization algorithms as the size of data sets and the computational complexity of scientific models continue to increase. As thes...
Travis J. Desell, David P. Anderson, Malik Magdon-...
GECCO
2010
Springer
173views Optimization» more  GECCO 2010»
15 years 5 months ago
The baldwin effect in developing neural networks
The Baldwin Effect is a very plausible, but unproven, biological theory concerning the power of learning to accelerate evolution. Simple computational models in the 1980’s gave...
Keith L. Downing
IWANN
2001
Springer
15 years 5 months ago
Evolving RBF Neural Networks
This paper is focused on determining the parameters of radial basis function neural networks (number of neurons, and their respective centers and radii) automatically. While this ...
Víctor Manuel Rivas Santos, Pedro A. Castil...
SEAL
1998
Springer
15 years 5 months ago
Robust Evolution Strategies
This paper empirically investigates the use and behaviour of Evolution Strategies (ES) algorithms on problems such as function optimisation and the use of evolutionary artificial ...
Kazuhiro Ohkura, Yoshiyuki Matsumura, Kanji Ueda