Efficient Evolution of High Entropy RNGs Using Single Node Genetic Programming

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@InProceedings{Leonard:2015:GECCO,
  author =       "Philip Leonard and David Jackson",
  title =        "Efficient Evolution of High Entropy RNGs Using Single
                 Node Genetic Programming",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1071--1078",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754820",
  DOI =          "doi:10.1145/2739480.2754820",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Random Number Generators are an important aspect of
                 many modern day software systems, cryptographic
                 protocols and modelling techniques. To be more
                 accurate, it is Pseudo Random Number Generators (PRNGs)
                 that are more commonly used over their expensive, and
                 less practical hardware based counterparts. Given that
                 PRNGs rely on some deterministic algorithm (typically a
                 Linear Congruential Generator) we can leverage
                 Shannon's theory of information as our fitness function
                 in order to generate these algorithms by evolutionary
                 means. In this paper we compare traditional Genetic
                 Programming (GP) against its graph based
                 implementation, Single Node Genetic Programming (SNGP),
                 for this task. We show that with SNGPs unique program
                 structure and use of dynamic programming, it is
                 possible to obtain smaller, higher entropy PRNGs, over
                 six times faster and produced at a solution rate twice
                 that achieved using Koza's standard GP model. We also
                 show that the PRNGs obtained from evolutionary methods
                 produce higher entropy outputs than other widely used
                 PRNGs and Hardware RNGs (specifically recordings of
                 atmospheric noise), as well as surpassing them in a
                 variety of other statistical tests presented in the
                 NIST RNG test suite.",
  notes =        "Also known as \cite{2754820} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",
}

Genetic Programming entries for Philip Leonard David Jackson

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