Lamar: A New Pseudorandom Number Generator Evolved by Means of Genetic Programming

  • Carlos Lamenca-Martinez
  • Julio Cesar Hernandez-Castro
  • Juan M. Estevez-Tapiador
  • Arturo Ribagorda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


Pseudorandom number generation is a key component of many Computer Science algorithms, including mathematical modeling, stochastic processes, Monte Carlo simulations, and most cryptographic primitives and protocols. To date, multiple approaches that use Evolutionary Computation (EC) techniques have been proposed for designing useful Pseudorandom Number Generators (PRNGs) for certain non-cryptographic applications. However, none of the proposals have been secure nor efficient enough to be of interest for the much more demanding crypto world. In this work, we present a general scheme, which uses Genetic Programming (GP), for the automatic design of crypto-quality PRNGs by evolving highly nonlinear and extremely efficient functions. A new PRNG named Lamar and obtained using this scheme is proposed, whose C code and preliminary security analysis are provided.


Genetic Programming Cellular Automaton Block Cipher Stream Cipher Pseudorandom Number Generator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Carlos Lamenca-Martinez
    • 1
  • Julio Cesar Hernandez-Castro
    • 1
  • Juan M. Estevez-Tapiador
    • 1
  • Arturo Ribagorda
    • 1
  1. 1.Computer Science DepartmentCarlos III University of Madrid 

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