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» Modelling Variability for System Families
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GECCO
2008
Springer
186views Optimization» more  GECCO 2008»
13 years 8 months ago
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate...
A. K. M. Khaled Ahsan Talukder, Michael Kirley, Ra...
BMCBI
2006
130views more  BMCBI 2006»
13 years 7 months ago
CARMA: A platform for analyzing microarray datasets that incorporate replicate measures
Background: The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust ex...
Kevin A. Greer, Matthew R. McReynolds, Heddwen L. ...
CCE
2007
13 years 7 months ago
A systematic approach for soft sensor development
This paper presents a systematic approach based on robust statistical techniques for development of a data-driven soft sensor, which is an important component of the process analy...
Bao Lin, Bodil Recke, Jørgen K. H. Knudsen,...
FSS
2010
147views more  FSS 2010»
13 years 6 months ago
A divide and conquer method for learning large Fuzzy Cognitive Maps
Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling and simulating dynamic systems. FCMs were applied in a large number of diverse areas and have already gained momentu...
Wojciech Stach, Lukasz A. Kurgan, Witold Pedrycz
BMCBI
2011
12 years 11 months ago
Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis
Background: Analysis of Cerebrospinal Fluid (CSF) samples holds great promise to diagnose neurological pathologies and gain insight into the molecular background of these patholog...
Lionel Blanchet, Agnieszka Smolinska, Amos Attali,...