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GECCO
2008
Springer
148views Optimization» more  GECCO 2008»
15 years 25 days ago
Accelerating convergence using rough sets theory for multi-objective optimization problems
We propose the use of rough sets theory to improve the first approximation provided by a multi-objective evolutionary algorithm and retain the nondominated solutions using a new ...
Luis V. Santana-Quintero, Carlos A. Coello Coello
GECCO
2006
Springer
144views Optimization» more  GECCO 2006»
15 years 3 months ago
On semi-supervised clustering via multiobjective optimization
Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. Semi-supervised clu...
Julia Handl, Joshua D. Knowles
BMCBI
2011
14 years 6 months ago
NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins
Background: Most predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two met...
Daniel Restrepo-Montoya, Camilo Pino, Luis F. Ni&n...
97
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BMCBI
2007
137views more  BMCBI 2007»
14 years 12 months ago
Bayesian refinement of protein functional site matching
Background: Matching functional sites is a key problem for the understanding of protein function and evolution. The commonly used graph theoretic approach, and other related appro...
Kanti V. Mardia, Vysaul B. Nyirongo, Peter J. Gree...
108
Voted
CEC
2007
IEEE
15 years 3 months ago
Development and validation of different hybridization strategies between GA and PSO
In this paper a new class of hybridization strategies between GA and PSO is presented and validated. The Genetical Swarm Optimization (GSO) approach is presented here with respect ...
A. Gandelli, F. Grimaccia, Marco Mussetta, Paola P...