Title

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.

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.

This document is currently not available here.

Share

 
COinS