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CEC
2007
IEEE
15 years 7 months ago
Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms
In this work we compare the use of a Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA) (both augmented with Support Vector Machines SVM) for the classification of high...
Enrique Alba, José García-Nieto, Lae...
100
Voted
GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
15 years 7 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
GECCO
2008
Springer
103views Optimization» more  GECCO 2008»
15 years 4 months ago
Empirical investigations on parallel competent genetic algorithms
This paper empirically investigates parallel competent genetic algorithms (cGAs) [4]. cGAs, such as BOA [21], LINCGA [15], D5 -GA [28], can solve GA-difficult problems by automati...
Miwako Tsuji, Masaharu Munetomo, Kiyoshi Akama
127
Voted
CORR
2010
Springer
320views Education» more  CORR 2010»
15 years 3 months ago
An algorithm for the principal component analysis of large data sets
Recently popularized randomized methods for principal component analysis (PCA) efficiently and reliably produce nearly optimal accuracy -- even on parallel processors -- unlike the...
Nathan Halko, Per-Gunnar Martinsson, Yoel Shkolnis...
108
Voted
CORR
2006
Springer
74views Education» more  CORR 2006»
15 years 3 months ago
Scheduling Algorithms for Procrastinators
This paper presents scheduling algorithms for procrastinators, where the speed that a procrastinator executes a job increases as the due date approaches. We give optimal off-line ...
Michael A. Bender, Raphaël Clifford, Kostas T...