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ASUNAM
2010
IEEE
13 years 3 months ago
A Multiobjective and Evolutionary Clustering Method for Dynamic Networks
—The discovery of evolving communities in dynamic networks is an important research topic that poses challenging tasks. Previous evolutionary based clustering methods try to maxi...
Francesco Folino, Clara Pizzuti
GECCO
2010
Springer
338views Optimization» more  GECCO 2010»
13 years 3 months ago
Multiobjective evolutionary community detection for dynamic networks
A multiobjective genetic algorithm for detecting communities in dynamic networks, i.e., networks that evolve over time, is proposed. The approach leverages on the concept of evolu...
Francesco Folino, Clara Pizzuti
CEC
2007
IEEE
13 years 11 months ago
On performance metrics and particle swarm methods for dynamic multiobjective optimization problems
— This paper describes two performance measures for measuring an EMO (Evolutionary Multiobjective Optimization) algorithm’s ability to track a time-varying Paretofront in a dyn...
Xiaodong Li, Jürgen Branke, Michael Kirley
GECCO
2010
Springer
187views Optimization» more  GECCO 2010»
13 years 8 months ago
Evolving agent behavior in multiobjective domains using fitness-based shaping
Multiobjective evolutionary algorithms have long been applied to engineering problems. Lately they have also been used to evolve behaviors for intelligent agents. In such applicat...
Jacob Schrum, Risto Miikkulainen
CEC
2009
IEEE
13 years 11 months ago
Evolutionary image segmentation based on multiobjective clustering
— In the fields of image processing and recognition, image segmentation is an important basic technique in which an image is partitioned into multiple regions (sets of pixels). ...
Shinichi Shirakawa, Tomoharu Nagao