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GECCO
2004
Springer
118views Optimization» more  GECCO 2004»
13 years 10 months ago
Adaptive Sampling for Noisy Problems
Abstract. The usual approach to deal with noise present in many realworld optimization problems is to take an arbitrary number of samples of the objective function and use the samp...
Erick Cantú-Paz
ICML
2003
IEEE
13 years 10 months ago
Evolutionary MCMC Sampling and Optimization in Discrete Spaces
The links between genetic algorithms and population-based Markov Chain Monte Carlo (MCMC) methods are explored. Genetic algorithms (GAs) are well-known for their capability to opt...
Malcolm J. A. Strens
GECCO
2000
Springer
13 years 8 months ago
Sampling Bias and Search Space Boundary Extension in Real Coded Genetic Algorithms
In Real coded genetic algorithms, some crossover operators do not work well on functions which have their optimum at the corner of the search space. To cope with this problem, we ...
Shigeyoshi Tsutsui
ECCV
2002
Springer
14 years 7 months ago
A Markov Chain Monte Carlo Approach to Stereovision
We propose Markov chain Monte Carlo sampling methods to address uncertainty estimation in disparity computation. We consider this problem at a postprocessing stage, i.e. once the d...
Julien Sénégas
TIP
2011
124views more  TIP 2011»
13 years 3 days ago
A Geometric Method for Optimal Design of Color Filter Arrays
—A color filter array (CFA) used in a digital camera is a mosaic of spectrally selective filters, which allows only one color component to be sensed at each pixel. The missing ...
Pengwei Hao, Yan Li, Zhouchen Lin, Eric Dubois