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BMCBI
2008
142views more  BMCBI 2008»
13 years 8 months ago
Genetic weighted k-means algorithm for clustering large-scale gene expression data
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
Fang-Xiang Wu
GECCO
2005
Springer
132views Optimization» more  GECCO 2005»
14 years 1 months ago
Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem
Augmenting an evolutionary algorithm with knowledge of its target problem can yield a more effective algorithm, as this presentation illustrates. The Quadratic Knapsack Problem e...
Bryant A. Julstrom
GECCO
2000
Springer
131views Optimization» more  GECCO 2000»
13 years 12 months ago
A Genetic Algorithm with Tabu Search for Multimodal and Multiobjective Function Optimization
The integration of genetic algorithms (GAs) and tabu search is one of traditional problems in function optimization in the GA literature. However, most proposed methods have utili...
Setsuya Kurahashi, Takao Terano
GECCO
2008
Springer
143views Optimization» more  GECCO 2008»
13 years 9 months ago
Genetic algorithms for mentor-assisted evaluation function optimization
In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function’s parameters for computer chess. Our results show that using an approp...
Omid David-Tabibi, Moshe Koppel, Nathan S. Netanya...
ICIAP
2001
Springer
14 years 8 months ago
Global Optimization of Deformable Surface Meshes Based on Genetic Algorithms
Deformable models are by their formulation able to solve surface extraction problem from noisy volumetric image data encountered commonly in medical image analysis. However, this ...
Jussi Tohka