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ICGA
1997
133views Optimization» more  ICGA 1997»
15 years 7 months ago
Messy Genetic Algorithms for Subset Feature Selection
Subset Feature Selection problems can have severalattributes which may make Messy Genetic Algorithms an appropriateoptimization method. First, competitive solutions may often use ...
L. Darrell Whitley, J. Ross Beveridge, Cesar Guerr...
EMO
2005
Springer
194views Optimization» more  EMO 2005»
16 years 1 days ago
An EMO Algorithm Using the Hypervolume Measure as Selection Criterion
Abstract. The hypervolume measure is one of the most frequently applied measures for comparing the results of evolutionary multiobjective optimization algorithms (EMOA). The idea t...
Michael Emmerich, Nicola Beume, Boris Naujoks
PPSN
1998
Springer
15 years 10 months ago
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
This paper presents a novel evolutionary approach of approximating the shape of the Pareto-optimal set of multi-objective optimization problems. The evolutionary algorithm (EA) use...
Marco Laumanns, Günter Rudolph, Hans-Paul Sch...
IDEAL
2009
Springer
15 years 4 months ago
Implementation and Integration of Algorithms into the KEEL Data-Mining Software Tool
This work is related to the KEEL1 (Knowledge Extraction based on Evolutionary Learning) tool, a non-commercial software that supports data management, design of experiments and an ...
Alberto Fernández, Julián Luengo, Jo...
SODA
2010
ACM
190views Algorithms» more  SODA 2010»
16 years 3 months ago
Lower Bounds for Sparse Recovery
We consider the following k-sparse recovery problem: design an m ? n matrix A, such that for any signal x, given Ax we can efficiently recover ^x satisfying
khanh do ba, piotr indyk, eric price