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SAGA
2009
Springer
15 years 11 months ago
Bounds for Multistage Stochastic Programs Using Supervised Learning Strategies
We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible dec...
Boris Defourny, Damien Ernst, Louis Wehenkel
ISMB
1994
15 years 5 months ago
Stochastic Motif Extraction Using Hidden Markov Model
In this paper, westudy the application of an ttMM(hidden Markov model) to the problem of representing protein sequencesby a stochastic motif. Astochastic protein motif represents ...
Yukiko Fujiwara, Minoru Asogawa, Akihiko Konagaya
SODA
2012
ACM
229views Algorithms» more  SODA 2012»
13 years 6 months ago
Approximation algorithms for stochastic orienteering
In the Stochastic Orienteering problem, we are given a metric, where each node also has a job located there with some deterministic reward and a random size. (Think of the jobs as...
Anupam Gupta, Ravishankar Krishnaswamy, Viswanath ...
HAIS
2009
Springer
15 years 9 months ago
Economic Load Dispatch Using a Chemotactic Differential Evolution Algorithm
This paper presents a novel stochastic optimization approach to solve constrained economic load dispatch (ELD) problem using Hybrid Bacterial Foraging-Differential Evolution optimi...
Arijit Biswas, Sambarta Dasgupta, Bijaya K. Panigr...
SYNTHESE
2008
84views more  SYNTHESE 2008»
15 years 4 months ago
How experimental algorithmics can benefit from Mayo's extensions to Neyman-Pearson theory of testing
Although theoretical results for several algorithms in many application domains were presented during the last decades, not all algorithms can be analyzed fully theoretically. Exp...
Thomas Bartz-Beielstein