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ICC
2000
IEEE
15 years 7 months ago
Reduced-State BCJR-Type Algorithms
—In this paper, we propose a technique to reduce the number of trellis states in BCJR-type algorithms, i.e., algorithms with a structure similar to that of the well-known algorit...
Giulio Colavolpe, Gian Luigi Ferrari, Riccardo Rah...
136
Voted
GECCO
2006
Springer
188views Optimization» more  GECCO 2006»
15 years 6 months ago
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
This work describes a forward-looking approach for the solution of dynamic (time-changing) problems using evolutionary algorithms. The main idea of the proposed method is to combi...
Iason Hatzakis, David Wallace
CCE
2006
15 years 2 months ago
An efficient algorithm for large scale stochastic nonlinear programming problems
The class of stochastic nonlinear programming (SNLP) problems is important in optimization due to the presence of nonlinearity and uncertainty in many applications, including thos...
Y. Shastri, Urmila M. Diwekar
119
Voted
ML
2006
ACM
142views Machine Learning» more  ML 2006»
15 years 2 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....
113
Voted
TC
2008
15 years 2 months ago
Optimal and Practical Algorithms for Sorting on the PDM
Abstract. The Parallel Disks Model (PDM) has been proposed to alleviate the I/O bottleneck that arises in the processing of massive data sets. Sorting has been extensively studied ...
Sanguthevar Rajasekaran, Sandeep Sen