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» Solving Combinatorial Auctions Using Stochastic Local Search
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CP
2006
Springer
13 years 11 months ago
Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms
Abstract. Machine learning can be utilized to build models that predict the runtime of search algorithms for hard combinatorial problems. Such empirical hardness models have previo...
Frank Hutter, Youssef Hamadi, Holger H. Hoos, Kevi...
GECCO
2007
Springer
258views Optimization» more  GECCO 2007»
13 years 11 months ago
TFBS identification by position- and consensus-led genetic algorithm with local filtering
Identification of Transcription Factor Binding Site (TFBS) motifs in multiple DNA upstream sequences is important in understanding the mechanism of gene regulation. This identific...
Tak-Ming Chan, Kwong-Sak Leung, Kin-Hong Lee
GECCO
2008
Springer
141views Optimization» more  GECCO 2008»
13 years 8 months ago
A study of NK landscapes' basins and local optima networks
We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces [5]. We use the well-known fami...
Gabriela Ochoa, Marco Tomassini, Sébastien ...
COR
2010
177views more  COR 2010»
13 years 7 months ago
Hybridization of very large neighborhood search for ready-mixed concrete delivery problems
Companies in the concrete industry are facing the following scheduling problem on a daily basis: concrete produced at several plants has to be delivered at customers' constru...
Verena Schmid, Karl F. Doerner, Richard F. Hartl, ...
ICDCS
2010
IEEE
13 years 11 months ago
Stochastic Steepest-Descent Optimization of Multiple-Objective Mobile Sensor Coverage
—We propose a steepest descent method to compute optimal control parameters for balancing between multiple performance objectives in stateless stochastic scheduling, wherein the ...
Chris Y. T. Ma, David K. Y. Yau, Nung Kwan Yip, Na...