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APIN
2000
120views more  APIN 2000»
13 years 7 months ago
Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes
Genetic algorithms are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with t...
Francisco Herrera, Manuel Lozano
GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
14 years 4 days 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....
CORR
2011
Springer
217views Education» more  CORR 2011»
13 years 2 months ago
Boolean Networks Design by Genetic Algorithms
Abstract. We present and discuss the results of an experimental analysis in the design of Boolean networks by means of genetic algorithms. A population of networks is evolved with ...
Andrea Roli, Cristian Arcaroli, Marco Lazzarini, S...
FOGA
1998
13 years 8 months ago
Understanding Interactions among Genetic Algorithm Parameters
Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex interactions among their parameters. For last two decades, researchers have been tr...
Kalyanmoy Deb, Samir Agrawal
TSMC
2002
93views more  TSMC 2002»
13 years 7 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...