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GECCO
2008
Springer
238views Optimization» more  GECCO 2008»
13 years 8 months ago
Using multiple offspring sampling to guide genetic algorithms to solve permutation problems
The correct choice of an evolutionary algorithm, a genetic representation for the problem being solved (as well as their associated variation operators) and the appropriate values...
Antonio LaTorre, José Manuel Peña, V...
BMCBI
2006
113views more  BMCBI 2006»
13 years 7 months ago
GibbsST: a Gibbs sampling method for motif discovery with enhanced resistance to local optima
Background: Computational discovery of transcription factor binding sites (TFBS) is a challenging but important problem of bioinformatics. In this study, improvement of a Gibbs sa...
Kazuhito Shida
GECCO
2008
Springer
184views Optimization» more  GECCO 2008»
13 years 8 months ago
Analysis of mammography reports using maximum variation sampling
A genetic algorithm (GA) was developed to implement a maximum variation sampling technique to derive a subset of data from a large dataset of unstructured mammography reports. It ...
Robert M. Patton, Barbara G. Beckerman, Thomas E. ...
EC
2002
228views ECommerce» more  EC 2002»
13 years 7 months ago
Improved Sampling of the Pareto-Front in Multiobjective Genetic Optimizations by Steady-State Evolution: A Pareto Converging Gen
Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we pre...
Rajeev Kumar, Peter Rockett
CEC
2010
IEEE
13 years 6 months ago
Gaussian Adaptation as a unifying framework for continuous black-box optimization and adaptive Monte Carlo sampling
Abstract— We present a unifying framework for continuous optimization and sampling. This framework is based on Gaussian Adaptation (GaA), a search heuristic developed in the late...
Christian L. Müller, Ivo F. Sbalzarini