Automated Design of Multi-Level Network Partitioning Heuristics Employing Self-Adaptive Primitive Granularity Control

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Pope:2020:GECCO,

• author = "Aaron Scott Pope and Daniel R. Tauritz",
• title = "Automated Design of Multi-Level Network Partitioning Heuristics Employing Self-Adaptive Primitive Granularity Control",
• year = "2020",
• editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann",
• isbn13 = "9781450371285",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• URL = "https://doi.org/10.1145/3377930.3389819",
• DOI = "doi:10.1145/3377930.3389819",
• booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference",
• pages = "1168--1176",
• size = "9 pages",
• keywords = "genetic algorithms, genetic programming, primitive granularity control, generative hyper-heuristic, graph partitioning, network segmentation",
• address = "internet",
• series = "GECCO '20",
• month = jul # " 8-12",
• organisation = "SIGEVO",
• abstract = "Network segmentation has a variety of applications, including computer network security. A well segmented computer network is less likely to result in information leaks and more resilient to adversarial traversal. Conventionally network segmentation approaches rely on graph partitioning algorithms. However, general-purpose graph partitioning solutions are just that, general purpose. These approaches do not exploit specific topological characteristics present in certain classes of networks. Tailored partition methods can be developed to target specific domains, but this process can be time consuming and difficult. This work builds on previous research employing generative hyper-heuristics in the form of genetic programming for automating the development of customized graph partitioning heuristics by incorporating a dynamic approach to controlling the granularity of the heuristic search. The potential of this approach is demonstrated using two real-world complex network applications. Results show that the automated design process is capable of fine tuning graph partitioning heuristics that sacrifice generality for improved performance on targeted networks.",
• notes = "Also known as \cite{10.1145/3377930.3389819} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Aaron S Pope Daniel R Tauritz

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