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» Evolutionary multiobjective optimization
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IJCNN
2006
IEEE
15 years 8 months ago
Ensemble Techniques for Avoiding Poor Performance in Evolved Neural Networks
— The idea of using evolutionary techniques to optimize the performance of neural networks is now widely used, but some approaches have been found to result in the evolution of r...
John A. Bullinaria
CEC
2005
IEEE
15 years 7 months ago
A study of dynamic severity in chaotic fitness landscapes
In this paper1 , we consider optimization by an evolutionary algorithm in dynamic environments that change chaotically. In particular, the concept of dynamic severity is applied to...
Hendrik Richter
GECCO
2004
Springer
127views Optimization» more  GECCO 2004»
15 years 7 months ago
Improved Niching and Encoding Strategies for Clustering Noisy Data Sets
Clustering is crucial to many applications in pattern recognition, data mining, and machine learning. Evolutionary techniques have been used with success in clustering, but most su...
Olfa Nasraoui, Elizabeth Leon
GECCO
2003
Springer
15 years 7 months ago
On Setting the Parameters of QEA for Practical Applications: Some Guidelines Based on Empirical Evidence
In this paper, some guidelines for setting the parameters of quantum-inspired evolutionary algorithm (QEA) are presented. Although the performance of QEA is excellent, there is rel...
Kuk-Hyun Han, Jong-Hwan Kim
CEC
2007
IEEE
15 years 8 months ago
An investigation into dynamic problem solving in a hybrid evolutionary market-based multi-agent system
— Static resources allocation problems have been widely studied. More recently some of this attention has changed to focus on dynamic problems, where problem specifications, con...
D. J. Cornforth