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GECCO
2008
Springer
186views Optimization» more  GECCO 2008»
13 years 6 months ago
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate...
A. K. M. Khaled Ahsan Talukder, Michael Kirley, Ra...
HAIS
2009
Springer
13 years 10 months ago
Multiobjective Evolutionary Clustering Approach to Security Vulnerability Assesments
Network vulnerability assessments collect large amounts of data to be further analyzed by security experts. Data mining and, particularly, unsupervised learning can help experts an...
Guiomar Corral, A. Garcia-Piquer, Albert Orriols-P...
GECCO
2006
Springer
188views Optimization» more  GECCO 2006»
13 years 9 months ago
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
This work describes a forward-looking approach for the solution of dynamic (time-changing) problems using evolutionary algorithms. The main idea of the proposed method is to combi...
Iason Hatzakis, David Wallace
CEC
2009
IEEE
14 years 1 days ago
A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design
Abstract— Designing efficient algorithms for difficult multiobjective optimization problems is a very challenging problem. In this paper a new clustering multi-objective evolut...
Yuping Wang, Chuangyin Dang, Hecheng Li, Lixia Han...
SBP
2011
Springer
13 years 7 days ago
Tracking Communities in Dynamic Social Networks
The study of communities in social networks has attracted considerable interest from many disciplines. Most studies have focused on static networks, and in doing so, have neglected...
Kevin S. Xu, Mark Kliger, Alfred O. Hero