Evolving Constructions for Balanced, Highly Nonlinear Boolean Functions
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{carlet:2022:GECCO,
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author = "Claude Carlet and Marko Djurasevic and
Domagoj Jakobovic and Luca Mariot and Stjepan Picek",
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title = "Evolving Constructions for Balanced, Highly Nonlinear
Boolean Functions",
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booktitle = "Proceedings of the 2022 Genetic and Evolutionary
Computation Conference",
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year = "2022",
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editor = "Alma Rahat and Jonathan Fieldsend and
Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and
Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and
Erik Hemberg and Christopher Cleghorn and Chao-li Sun and
Georgios Yannakakis and Nicolas Bredeche and
Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and
Sebastian Risi and Laetitia Jourdan and
Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and
John Woodward and Malcolm Heywood and Elizabeth Wanner and
Leonardo Trujillo and Domagoj Jakobovic and
Risto Miikkulainen and Bing Xue and Aneta Neumann and
Richard Allmendinger and Inmaculada Medina-Bulo and
Slim Bechikh and Andrew M. Sutton and
Pietro Simone Oliveto",
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pages = "1147--1155",
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address = "Boston, USA",
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series = "GECCO '22",
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month = "9-13 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, Boolean
functions, evolutionary algorithms, non-linearity,
secondary constructions, balancedness",
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isbn13 = "978-1-4503-9237-2",
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URL = "https://doi.org/10.1145/3512290.3528871",
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DOI = "doi:10.1145/3512290.3528871",
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video_url = "https://vimeo.com/725452312",
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abstract = "Finding balanced, highly nonlinear Boolean functions
is a difficult problem where it is not known what
nonlinearity values are possible to be reached in
general. At the same time, evolutionary computation is
successfully used to evolve specific Boolean function
instances, but the approach cannot easily scale for
larger Boolean function sizes. Indeed, while evolving
smaller Boolean functions is almost trivial, larger
sizes become increasingly difficult, and evolutionary
algorithms perform suboptimally.In this work, we ask
whether genetic programming (GP) can evolve
constructions resulting in balanced Boolean functions
with high nonlinearity. This question is especially
interesting as there are only a few known such
constructions. Our results show that GP can find
constructions that generalize well, i.e., result in the
required functions for multiple tested sizes. Further,
we show that GP evolves many equivalent constructions
under different syntactic representations.
Interestingly, the simplest solution found by GP is a
particular case of the well-known indirect sum
construction.",
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notes = "GECCO-2022 A Recombination of the 31st International
Conference on Genetic Algorithms (ICGA) and the 27th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Claude Carlet
Marko Durasevic
Domagoj Jakobovic
Luca Mariot
Stjepan Picek
Citations