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Evolving Multi-Level Graph Partitioning Algorithms
Proceedings of the IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2016 (2016, Athens, Greece)
  • Aaron S. Pope
  • Daniel R. Tauritz, Missouri University of Science and Technology
  • Alexander D. Kent
Abstract
Optimal graph partitioning is a foundational problem in computer science, and appears in many different applications. Multi-level graph partitioning is a state-of-the-art method of efficiently approximating high quality graph partitions. In this work, genetic programming techniques are used to evolve new multi-level graph partitioning heuristics that are tailored to specific applications. Results are presented using these evolved partitioners on traditional random graph models as well as a real-world computer network data set. These results demonstrate an improvement in the quality of the partitions produced over current state-of-the-art methods.
Meeting Name
IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2016 (2016: Dec. 6-9, Athens, Greece)
Department(s)
Computer Science
Research Center/Lab(s)
Center for High Performance Computing Research
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
12-1-2016
Citation Information
Aaron S. Pope, Daniel R. Tauritz and Alexander D. Kent. "Evolving Multi-Level Graph Partitioning Algorithms" Proceedings of the IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2016 (2016, Athens, Greece) (2016)
Available at: http://works.bepress.com/daniel-tauritz/66/