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» A New Crossover Operator for Genetic Algorithms
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BMCBI
2008
142views more  BMCBI 2008»
13 years 7 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
CEC
2005
IEEE
14 years 1 months ago
Coarse graining in an evolutionary algorithm with recombination, duplication and inversion
A generalised form of recombination, wherein an offspring can be formed from any of the genetic material of the parents, is analysed in the context of a two-locus recombinative G...
Christopher R. Stephens, Riccardo Poli
GECCO
2006
Springer
166views Optimization» more  GECCO 2006»
13 years 11 months ago
Comparing genetic robustness in generational vs. steady state evolutionary algorithms
Previous research has shown that evolutionary systems not only try to develop solutions that satisfy a fitness requirement, but indirectly attempt to develop genetically robust so...
Josh Jones, Terry Soule
IJIT
2004
13 years 9 months ago
Restartings: A Technique to Improve Classic Genetic Algorithms' Performance
In this contribution, a way to enhance the performance of the classic Genetic Algorithm is proposed. The idea of restarting a Genetic Algorithm is applied in order to obtain better...
Grigorios N. Beligiannis, Georgios A. Tsirogiannis...
GECCO
2003
Springer
14 years 24 days ago
A Kernighan-Lin Local Improvement Heuristic That Solves Some Hard Problems in Genetic Algorithms
We present a Kernighan-Lin style local improvement heuristic for genetic algorithms. We analyze the run-time cost of the heuristic. We demonstrate through experiments that the heur...
William A. Greene