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» Convergence Models of Genetic Algorithm Selection Schemes
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IWANN
2009
Springer
14 years 2 months ago
RCGA-S/RCGA-SP Methods to Minimize the Delta Test for Regression Tasks
Frequently, the number of input variables (features) involved in a problem becomes too large to be easily handled by conventional machine-learning models. This paper introduces a c...
Fernando Mateo, Dusan Sovilj, Rafael Gadea Giron&e...
BMCBI
2010
108views more  BMCBI 2010»
13 years 7 months ago
A genetic ensemble approach for gene-gene interaction identification
Background: It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases...
Pengyi Yang, Joshua W. K. Ho, Albert Y. Zomaya, Bi...
GECCO
2006
Springer
132views Optimization» more  GECCO 2006»
13 years 11 months ago
"Optimal" mutation rates for genetic search
Using a set of model landscapes we examine how different mutation rates affect different search metrics. We show that very universal heuristics, such as 1/N and the error threshol...
Jorge Cervantes, Christopher R. Stephens
CVPR
2004
IEEE
13 years 11 months ago
Face Localization via Hierarchical CONDENSATION with Fisher Boosting Feature Selection
We formulate face localization as a Maximum A Posteriori Probability(MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribu...
Jilin Tu, ZhenQiu Zhang, Zhihong Zeng, Thomas S. H...
HEURISTICS
2008
120views more  HEURISTICS 2008»
13 years 7 months ago
A local linear embedding module for evolutionary computation optimization
A Local Linear Embedding (LLE) module enhances the performance of two Evolutionary Computation (EC) algorithms employed as search tools in global optimization problems. The LLE em...
Fabio Boschetti