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» Optimal Control With Noisy Time
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DATE
2010
IEEE
183views Hardware» more  DATE 2010»
15 years 2 months ago
Toward optimized code generation through model-based optimization
—Model-Based Development (MBD) provides an al level of abstraction, the model, which lets engineers focus on the business aspect of the developed system. MBD permits automatic tr...
Asma Charfi, Chokri Mraidha, Sébastien G&ea...
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
15 years 2 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
Verena Heidrich-Meisner, Christian Igel
GECCO
2009
Springer
128views Optimization» more  GECCO 2009»
15 years 8 months ago
Evolving stochastic processes using feature tests and genetic programming
The synthesis of stochastic processes using genetic programming is investigated. Stochastic process behaviours take the form of time series data, in which quantities of interest v...
Brian J. Ross, Janine H. Imada
AMC
2010
69views more  AMC 2010»
15 years 4 months ago
The Leitmann-Schmitendorf advertising game with n players and time discounting
The extension of the Leitmann-Schmitendorf advertising game to n players and positive time discounting is investigated. We show that the strong time consistency of the open-loop N...
Davide Dragone, Luca Lambertini, Arsen Palestini
ECCV
2002
Springer
16 years 6 months ago
Regularized Shock Filters and Complex Diffusion
We address the issue of regularizing Osher and Rudin's shock filter, used for image deblurring, in order to allow processes that are more robust against noise. Previous soluti...
Guy Gilboa, Nir A. Sochen, Yehoshua Y. Zeevi