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CORR
2010
Springer
427views Education» more  CORR 2010»
14 years 11 months ago
A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algori
Genetic algorithms are a population-based Meta heuristics. They have been successfully applied to many optimization problems. However, premature convergence is an inherent charact...
Poonam Garg
109
Voted
GECCO
2000
Springer
15 years 4 months ago
Modeling GA Performance for Control Parameter Optimization
Optimization of the control parameters of genetic algorithms is often a time consuming and tedious task. In this work we take the meta-level genetic algorithm approach to control ...
Vincent A. Cicirello, Stephen F. Smith
89
Voted
ICSM
2003
IEEE
15 years 5 months ago
Library Miniaturization Using Static and Dynamic Information
Moving to smaller libraries can be considered as a relevant task when porting software systems to limited-resource devices (e.g., hand-helds). Library miniaturization will be part...
Giuliano Antoniol, Massimiliano Di Penta
103
Voted
BMCBI
2004
208views more  BMCBI 2004»
15 years 12 days ago
Hybrid clustering for microarray image analysis combining intensity and shape features
Background: Image analysis is the first crucial step to obtain reliable results from microarray experiments. First, areas in the image belonging to single spots have to be identif...
Jörg Rahnenführer, Daniel Bozinov
112
Voted
ISCC
2006
IEEE
188views Communications» more  ISCC 2006»
15 years 6 months ago
Dejong Function Optimization by Means of a Parallel Approach to Fuzzified Genetic Algorithm
Genetic Algorithms are very powerful search methods that are used in different optimization problems. Parallel versions of genetic algorithms are easily implemented and usually in...
Ebrahim Bagheri, Hossein Deldari