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» A New Crossover Operator for Genetic Algorithms
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137
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BMCBI
2008
142views more  BMCBI 2008»
15 years 3 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
68
Voted
CEC
2005
IEEE
15 years 9 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
109
Voted
GECCO
2006
Springer
166views Optimization» more  GECCO 2006»
15 years 7 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
121
Voted
IJIT
2004
15 years 5 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...
139
Voted
GECCO
2003
Springer
15 years 8 months 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