Finding State-of-the-Art Non-cryptographic Hashes with Genetic Programming

  • César Estébanez
  • Julio César Hernández-Castro
  • Arturo Ribagorda
  • Pedro Isasi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


The design of non-cryptographic hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this paper, we use the Genetic Programming paradigm to evolve collision free and fast hash functions. For achieving robustness against collision we use a fitness function based on a non-linearity concept, producing evolved hashes with a good degree of Avalanche Effect. The other main issue, efficiency, is assured by using only very fast operators (both in hardware and software) and by limiting the number of nodes. Using this approach, we have created a new hash function, which we call gp-hash, that is able to outperform a set of five human-generated, widely-used hash functions.


Genetic Program Hash Function Genetic Programming System Collision Test Strict Avalanche Criterion 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fowler, noll, vo. fnv hash web page,
  2. 2.
    The lil-gp genetic programming system is available at,
  3. 3.
    Berarducci, P., Jordan, D., Martin, D., Seitzer, J.: GEVOSH: Using grammatical evolution to generate hashing functions. In: Poli, R., Cagnoni, S., Keijzer, M., Costa, E., Pereira, F., Raidl, G., Upton, S.C., Goldberg, D., Lipson, H., de Jong, E., Koza, J., Suzuki, H., Sawai, H., Parmee, I., Pelikan, M., Sastry, K., Thierens, D., Stolzmann, W., Lanzi, P.L., Wilson, S.W., O’Neill, M., Ryan, C., Yu, T., Miller, J.F., Garibay, I., Holifield, G., Wu, A.S., Riopka, T., Meysenburg, M.M., Wright, A.W., Richter, N., Moore, J.H., Ritchie, M.D., Davis, L., Roy, R., Jakiela, M. (eds.) GECCO 2004 Workshop Proceedings, Seattle, Washington, USA, June 26-30 (2004)Google Scholar
  4. 4.
    Damiani, E., Liberali, V., Tettamanzi, A.G.B.: Evolutionary design of hashing function circuits using an FPGA, September 17 (1998)Google Scholar
  5. 5.
    Forré, R.: The strict avalanche criterion: spectral properties of boolean functions and an extended definition. In: Goldwasser, S. (ed.) CRYPTO 1988. LNCS, vol. 403, pp. 450–468. Springer, Heidelberg (1990)Google Scholar
  6. 6.
    Hinton, G., Sager, D., Upton, M., Boggs, D., Carmean, D., Kyker, A., Roussel, P.: The microarchitecture of the pentium 4 processor. Intel Technology Journal, Q1 (2001),
  7. 7.
    Hussain, D., Malliaris, S.: Evolutionary techniques applied to hashing: An efficient data retrieval method. In: Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.-G. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), Las Vegas, Nevada, July 10-12, 2000, p. 760. Morgan Kaufmann, San Francisco (2000)Google Scholar
  8. 8.
    Jenkins, B.: A hash function for hash table lookup. Dr. Dobbs Journal (September 1997)Google Scholar
  9. 9.
    Knuth, D.: The Art of Computer Programming. Addison-Wesley, Reading (1998)Google Scholar
  10. 10.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  11. 11.
    Matsumoto, Nishimura: Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACMTMCS: ACM Transactions on Modeling and Computer Simulation 8 (1998)Google Scholar
  12. 12.
    Wheeler, D.J., Needham, R.M.: TEA, a tiny encryption algorithm. LNCS, vol. 1008, pp. 363–369. Springer, Heidelberg (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • César Estébanez
    • 1
  • Julio César Hernández-Castro
    • 1
  • Arturo Ribagorda
    • 1
  • Pedro Isasi
    • 1
  1. 1.Universidad Carlos III de MadridLeganés (Madrid)Spain

Personalised recommendations