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» Parallel Strategies for Stochastic Evolution
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ICPP
2007
IEEE
14 years 1 months ago
Multi-Layer Event Trace Analysis for Parallel I/O Performance Tuning
The complexity of parallel I/O systems lies in the deep I/O stack with many software layers and concurrent I/O request handling at multiple layers. This paper explores multi-layer...
Pin Lu, Kai Shen
PPSN
2004
Springer
14 years 25 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
PPSN
1998
Springer
13 years 11 months ago
Comparison of Evolutionary Algorithms for Design Optimization
The production of specimen for microsystems or microcomponents is both, time and material-consuming. In a traditional design process the number of possible variations which can be ...
Wilfried Jakob, Martina Gorges-Schleuter, Ingo Sie...
IPPS
2000
IEEE
13 years 12 months ago
Run-Time Support for Adaptive Load Balancing
Abstract. Many parallel scienti c applications have dynamic and irregular computational structure. However, most such applications exhibit persistence of computational load and com...
Milind A. Bhandarkar, Robert Brunner, Laxmikant V....
GECCO
2010
Springer
187views Optimization» more  GECCO 2010»
13 years 11 months ago
Benchmarking the (1, 4)-CMA-ES with mirrored sampling and sequential selection on the noisy BBOB-2010 testbed
The Covariance-Matrix-Adaptation Evolution-Strategy (CMA-ES) is a robust stochastic search algorithm for optimizing functions defined on a continuous search space RD . Recently, ...
Anne Auger, Dimo Brockhoff, Nikolaus Hansen