Evolving Multi-Level Graph Partitioning Algorithms
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.
Recommended Citation
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.
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
Second Research Center/Lab
Intelligent Systems Center
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
01 Dec 2016
