Evolving Multi-Level Graph Partitioning Algorithms
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.
A. S. Pope et al., "Evolving Multi-Level Graph Partitioning Algorithms," Proceedings of the IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2016 (2016, Athens, Greece), Institute of Electrical and Electronics Engineers (IEEE), Dec 2016.
IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2016 (2016: Dec. 6-9, Athens, Greece)
Center for High Performance Computing Research
Second Research Center/Lab
Intelligent Systems Center
Article - Conference proceedings
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01 Dec 2016