Automated Design of Multi-Level Network Partitioning Heuristics Employing Self-Adaptive Primitive Granularity Control
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Pope:2020:GECCO,
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author = "Aaron Scott Pope and Daniel R. Tauritz",
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title = "Automated Design of Multi-Level Network Partitioning
Heuristics Employing Self-Adaptive Primitive
Granularity Control",
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year = "2020",
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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",
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isbn13 = "9781450371285",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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URL = "https://doi.org/10.1145/3377930.3389819",
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DOI = "doi:10.1145/3377930.3389819",
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booktitle = "Proceedings of the 2020 Genetic and Evolutionary
Computation Conference",
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pages = "1168--1176",
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size = "9 pages",
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keywords = "genetic algorithms, genetic programming, primitive
granularity control, generative hyper-heuristic, graph
partitioning, network segmentation",
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address = "internet",
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series = "GECCO '20",
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month = jul # " 8-12",
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organisation = "SIGEVO",
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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.",
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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
Citations