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» Using Gaussian Processes to Optimize Expensive Functions
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PPSN
2004
Springer
14 years 29 days ago
LS-CMA-ES: A Second-Order Algorithm for Covariance Matrix Adaptation
Abstract. Evolution Strategies, Evolutionary Algorithms based on Gaussian mutation and deterministic selection, are today considered the best choice as far as parameter optimizatio...
Anne Auger, Marc Schoenauer, Nicolas Vanhaecke
VLSID
2007
IEEE
149views VLSI» more  VLSID 2007»
14 years 8 months ago
Efficient and Accurate Statistical Timing Analysis for Non-Linear Non-Gaussian Variability With Incremental Attributes
Title of thesis: EFFICIENT AND ACCURATE STATISTICAL TIMING ANALYSIS FOR NON-LINEAR NON-GAUSSIAN VARIABILITY WITH INCREMENTAL ATTRIBUTES Ashish Dobhal, Master of Science, 2006 Thes...
Ashish Dobhal, Vishal Khandelwal, Ankur Srivastava
CEC
2011
IEEE
12 years 7 months ago
Accelerating convergence towards the optimal pareto front
—Evolutionary algorithms have been very popular optimization methods for a wide variety of applications. However, in spite of their advantages, their computational cost is still ...
Mohsen Davarynejad, Jafar Rezaei, Jos L. M. Vranck...
TPDS
1998
118views more  TPDS 1998»
13 years 7 months ago
Optimizing Computing Costs Using Divisible Load Analysis
—A bus oriented network where there is a charge for the amount of divisible load processed on each processor is investigated. A cost optimal processor sequencing result is found ...
Jeeho Sohn, Thomas G. Robertazzi, Serge Luryi
SSPR
2004
Springer
14 years 29 days ago
Feature Subset Selection Using an Optimized Hill Climbing Algorithm for Handwritten Character Recognition
This paper presents an optimized Hill Climbing algorithm to select a subset of features for handwritten character recognition. The search is conducted taking into account a random ...
Carlos M. Nunes, Alceu de Souza Britto Jr., Celso ...