Evolving Multi-Level Graph Partitioning AlgorithmsProceedings of the IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2016 (2016, Athens, Greece)
AbstractOptimal 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 NameIEEE Symposium Series on Computational Intelligence, IEEE SSCI 2016 (2016: Dec. 6-9, Athens, Greece)
Research Center/Lab(s)Center for High Performance Computing Research
Document TypeArticle - Conference proceedings
Rights© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Citation InformationAaron 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/