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» Comparative analysis of genetic algorithm implementations
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GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
13 years 11 months ago
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
The Negative Slope Coefficient (nsc) is an empirical measure of problem hardness based on the analysis of offspring-fitness vs. parent-fitness scatterplots. The nsc has been teste...
Riccardo Poli, Leonardo Vanneschi
ECCV
2008
Springer
14 years 9 months ago
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction o...
Rahul Raguram, Jan-Michael Frahm, Marc Pollefeys
AEI
2005
99views more  AEI 2005»
13 years 7 months ago
Comparison among five evolutionary-based optimization algorithms
Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed ...
Emad Elbeltagi, Tarek Hegazy, Donald E. Grierson
EC
2008
120views ECommerce» more  EC 2008»
13 years 7 months ago
Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments
In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants...
Shengxiang Yang
WOB
2004
233views Bioinformatics» more  WOB 2004»
13 years 8 months ago
Recent Advances in Gene Expression Data Clustering: A Case Study with Comparative Results
Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the...
George Barreto Bezerra, Geraldo M. A. Cança...