On Generalizing the Power Function Exponent                  Constructions with Genetic Programming 
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- @InProceedings{carlet:2022:GECCOcomp,
- 
  author =       "Claude Carlet and Domagoj Jakobovic and 
Stjepan Picek",
- 
  title =        "On Generalizing the Power Function Exponent
Constructions with Genetic Programming",
- 
  booktitle =    "Proceedings of the 2022 Genetic and Evolutionary
Computation Conference Companion",
- 
  year =         "2022",
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  editor =       "Heike Trautmann and Carola Doerr and 
Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and 
Marcus Gallagher and Yew-Soon Ong and 
Abhishek Gupta and Anna V Kononova and Hao Wang and 
Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and 
Fabio Caraffini and Johann Dreo and Anne Auger and 
Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and 
Nikolaus Hansen and Olaf Mersmann and Petr Posik and 
Tea Tusar and Dimo Brockhoff and Tome Eftimov and 
Pascal Kerschke and Boris Naujoks and Mike Preuss and 
Vanessa Volz and Bilel Derbel and Ke Li and 
Xiaodong Li and Saul Zapotecas and Qingfu Zhang and 
Mark Coletti and Catherine (Katie) Schuman and 
Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and 
Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and 
Richard Allmendinger and Jussi Hakanen and 
Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and 
John McCall and Jaume Bacardit and 
Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and 
David Walker and Jamal Toutouh and UnaMay O'Reilly and 
Penousal Machado and Joao Correia and Sergio Nesmachnow and 
Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and 
Francisco {Fernandez de Vega} and Giuseppe Paolo and 
Alex Coninx and Antoine Cully and Adam Gaier and 
Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and 
Vesna Nowack and Aymeric Blot and Emily Winter and 
William B. Langdon and Justyna Petke and 
Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and 
Thomas Stuetzle and David Paetzel and 
Alexander Wagner and Michael Heider and Nadarajen Veerapen and 
Katherine Malan and Arnaud Liefooghe and Sebastien Verel and 
Gabriela Ochoa and Mohammad Nabi Omidvar and 
Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and 
Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and 
Jean-Baptiste Mouret and Stephane Doncieux and 
Stefanos Nikolaidis and Julian Togelius and 
Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and 
Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and 
Ofer Shir and Lee Spector and Alma Rahat and 
Richard Everson and Jonathan Fieldsend and Handing Wang and 
Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and 
Michael Kommenda and William {La Cava} and 
Gabriel Kronberger and Steven Gustafson",
- 
  pages =        "691--694",
- 
  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, evolutionary
algorithms, APN, power function, s-box, almost perfect
nonlinear",
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  isbn13 =       "978-1-4503-9268-6/22/07",
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  DOI =          " 10.1145/3520304.3529081", 10.1145/3520304.3529081",
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  abstract =     "Many works are investigating Almost Perfect Nonlinear
(APN) functions and, in particular, APN power
functions. Such functions are of the form F(x) = xd,
and they have practical relevance as they reach in
characteristic 2 the best possible differential
uniformity. This work investigates whether genetic
programming (GP) can {"}reinvent{"} the known
expressions used to obtain exponent values d resulting
in APN functions. The ultimate goal is to find classes
of exponents that would be {"}transversal{"} to the
known infinite classes of APN exponents, and would
contain new APN exponents (for values of n necessarily
larger than those for which an exhaustive search could
be made so far). This would be already a breakthrough,
and our hope is to find this way new infinite classes
of APN exponents. Our results show this is possible but
difficult, and a careful trade-off between finding new
values and skipping known values is required.",
- 
  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
Domagoj Jakobovic
Stjepan Picek
Citations
