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ICDM
2005
IEEE
116views Data Mining» more  ICDM 2005»
14 years 1 months ago
Learning Functional Dependency Networks Based on Genetic Programming
Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle dis...
Wing-Ho Shum, Kwong-Sak Leung, Man Leung Wong
WAC
2005
Springer
118views Communications» more  WAC 2005»
14 years 1 months ago
Experiments on the Automatic Evolution of Protocols Using Genetic Programming
Truly autonomic networks ultimately require self-modifying, evolving protocol software. Otherwise humans must intervene in every situation that has not been anticipated at design t...
Lidia Yamamoto, Christian F. Tschudin
EUROGP
2004
Springer
133views Optimization» more  EUROGP 2004»
14 years 1 months ago
Lymphoma Cancer Classification Using Genetic Programming with SNR Features
Lymphoma cancer classification with DNA microarray data is one of important problems in bioinformatics. Many machine learning techniques have been applied to the problem and produc...
Jin-Hyuk Hong, Sung-Bae Cho
GECCO
2004
Springer
14 years 1 months ago
On the Strength of Size Limits in Linear Genetic Programming
Abstract. Bloat is a common and well studied problem in genetic programming. Size and depth limits are often used to combat bloat, but to date there has been little detailed explor...
Nicholas Freitag McPhee, Alex Jarvis, Ellery Fusse...
GECCO
2004
Springer
113views Optimization» more  GECCO 2004»
14 years 1 months ago
Implications of Epigenetic Learning Via Modification of Histones on Performance of Genetic Programming
Extending the notion of inheritable genotype in genetic programming (GP) from the common model of DNA into chromatin (DNA and histones), we propose an approach of embedding in GP a...
Ivan Tanev, Kikuo Yuta