Abstract
This paper presents a hierarchical and easy configurable framework for the implementation of distributed evolutionary algorithms for multiobjective optimization problems. The proposed approach is based on a layered structure corresponding to different execution environments like single computers, computing clusters and grid infrastructures. Two case studies, one based on a classical test suite in multiobjective optimization and one based on a data mining task, are presented and the results obtained both on a local cluster of computers and in a grid environment illustrates the characteristics of the proposed implementation framework.
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Zaharie, D., Petcu, D., Panica, S. (2008). A Hierarchical Approach in Distributed Evolutionary Algorithms for Multiobjective Optimization. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2007. Lecture Notes in Computer Science, vol 4818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78827-0_59
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DOI: https://doi.org/10.1007/978-3-540-78827-0_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78825-6
Online ISBN: 978-3-540-78827-0
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