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» Hybrid Optimizations: Which Optimization Algorithm to Use
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GECCO
1999
Springer
133views Optimization» more  GECCO 1999»
13 years 11 months ago
Forecasting the MagnetoEncephaloGram (MEG) of Epileptic Patients Using Genetically Optimized Neural Networks
In this work MagnetoEncephaloGram (MEG) recordings of epileptic patients were analyzed using a hybrid neural networks training algorithm. This algorithm combines genetic algorithm...
Adam V. Adamopoulos, Efstratios F. Georgopoulos, S...
QOSA
2010
Springer
13 years 5 months ago
A Hybrid Approach for Multi-attribute QoS Optimisation in Component Based Software Systems
Design decisions for complex, component-based systems impact multiple quality of service (QoS) properties. Often, means to improve one quality property deteriorate another one. In ...
Anne Martens, Danilo Ardagna, Heiko Koziolek, Raff...
JPDC
2006
133views more  JPDC 2006»
13 years 7 months ago
Grid computing for parallel bioinspired algorithms
This paper focuses on solving large size combinatorial optimization problems using a Grid-enabled framework called ParadisEO
Nouredine Melab, Sébastien Cahon, El-Ghazal...
GECCO
2003
Springer
139views Optimization» more  GECCO 2003»
14 years 23 days ago
Daily Stock Prediction Using Neuro-genetic Hybrids
We propose a neuro-genetic daily stock prediction model. Traditional indicators of stock prediction are utilized to produce useful input features of neural networks. The genetic al...
Yung-Keun Kwon, Byung Ro Moon
BMCBI
2006
127views more  BMCBI 2006»
13 years 7 months ago
Improvement in the Reproducibility and Accuracy of DNA Microarray Quantification by Optimizing Hybridization Conditions
Background: DNA microarrays, which have been increasingly used to monitor mRNA transcripts at a global level, can provide detailed insight into cellular processes involved in resp...
Tao Han, Cathy D. Melvin, Leming M. Shi, William S...