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TSMC
2002
93views more  TSMC 2002»
13 years 4 months ago
Statistical analysis of the main parameters involved in the design of a genetic algorithm
Abstract--Most genetic algorithm (GA) users adjust the main parameters of the design of a GA (crossover and mutation probability, population size, number of generations, crossover,...
Ignacio Rojas, Jesús González, H&eac...
GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
13 years 9 months ago
Initialization parameter sweep in ATHENA: optimizing neural networks for detecting gene-gene interactions in the presence of sma
Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved insufficient i...
Emily Rose Holzinger, Carrie C. Buchanan, Scott M....
JMLR
2010
194views more  JMLR 2010»
12 years 11 months ago
A Statistical Implicative Analysis Based Algorithm and MMPC Algorithm for Detecting Multiple Dependencies
Discovering the dependencies among the variables of a domain from examples is an important problem in optimization. Many methods have been proposed for this purpose, but few large...
Elham Salehi, Jayashree Nyayachavadi, Robin Gras
BMCBI
2008
111views more  BMCBI 2008»
13 years 5 months ago
MLIP: using multiple processors to compute the posterior probability of linkage
Background: Localization of complex traits by genetic linkage analysis may involve exploration of a vast multidimensional parameter space. The posterior probability of linkage (PP...
Manika Govil, Alberto Maria Segre, Veronica J. Vie...
GECCO
2005
Springer
131views Optimization» more  GECCO 2005»
13 years 10 months ago
Statistical analysis of heuristics for evolving sorting networks
Designing efficient sorting networks has been a challenging combinatorial optimization problem since the early 1960’s. The application of evolutionary computing to this problem ...
Lee K. Graham, Hassan Masum, Franz Oppacher