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» A hybrid approach to parameter tuning in genetic algorithms
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HIS
2001
13 years 9 months ago
Global Optimisation of Neural Networks Using a Deterministic Hybrid Approach
Selection of the topology of a neural network and correct parameters for the learning algorithm is a tedious task for designing an optimal artificial neural...
Gleb Beliakov, Ajith Abraham
AIR
2004
130views more  AIR 2004»
13 years 7 months ago
A Hybrid Framework for Over-Constrained Generalized
Abstract. In this work we study an over-constrained scheduling problem where constraints cannot be relaxed. This problem originates from a local defense agency where activities to ...
Andrew Lim, Brian Rodrigues, Ramesh Thangarajo, Fe...
CMSB
2006
Springer
13 years 11 months ago
Possibilistic Approach to Biclustering: An Application to Oligonucleotide Microarray Data Analysis
Abstract. The important research objective of identifying genes with similar behavior with respect to different conditions has recently been tackled with biclustering techniques. I...
Maurizio Filippone, Francesco Masulli, Stefano Rov...
EUROGP
2006
Springer
138views Optimization» more  EUROGP 2006»
13 years 11 months ago
Evolving Crossover Operators for Function Optimization
Abstract. A new model for evolving crossover operators for evolutionary function optimization is proposed in this paper. The model is a hybrid technique that combines a Genetic Pro...
Laura Diosan, Mihai Oltean
IVC
2008
74views more  IVC 2008»
13 years 7 months ago
Log-polar mapping template design: From task-level requirements to geometry parameters
The best parameters defining the geometry of a visual sensor generally depend on the particular visual task the sensor is intended to be used in. However, translating task require...
V. Javier Traver, Filiberto Pla