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» MDGA: motif discovery using a genetic algorithm
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BMCBI
2006
202views more  BMCBI 2006»
13 years 8 months ago
Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks
Background: The learning of global genetic regulatory networks from expression data is a severely under-constrained problem that is aided by reducing the dimensionality of the sea...
David J. Reiss, Nitin S. Baliga, Richard Bonneau
FLAIRS
2001
13 years 9 months ago
A Framework for Evolving Fuzzy Classifier Systems Using Genetic Programming
Afuzzy classifier systemframeworkis proposedwhich employsa tree-based representation for fuzzy rule (classifier) antecedents and genetic programmingfor fuzzy rule discovery. Sucha...
Brian Carse, Anthony G. Pipe
KES
1998
Springer
14 years 3 days ago
Properties of robust solution searching in multi-dimensional space with genetic algorithms
-A large number of studies on Genetic Algorithms (GAs) emphasize finding a globally optimal solution. Some other investigations have also been made for detecting multiple solutions...
Shigeyoshi Tsutsui, Lakhmi C. Jain
GECCO
2004
Springer
151views Optimization» more  GECCO 2004»
14 years 1 months ago
Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming
In this paper we show how genetic programming can be used to discover useful texture feature extraction algorithms. Grey level histograms of different textures are used as inputs ...
Brian T. Lam, Victor Ciesielski
GECCO
2004
Springer
175views Optimization» more  GECCO 2004»
14 years 1 months ago
Enhanced Innovation: A Fusion of Chance Discovery and Evolutionary Computation to Foster Creative Processes and Decision Making
Abstract. Human-based genetic algorithms are powerful tools for organizational modeling. If we enhance them using chance discovery techniques, we obtain an innovative approach for ...
Xavier Llorà, Kei Ohnishi, Ying-Ping Chen, ...