Abstract
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.
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Estébanez, C., Hernández-Castro, J.C., Ribagorda, A., Isasi, P. (2006). Finding State-of-the-Art Non-cryptographic Hashes with Genetic Programming. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_83
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DOI: https://doi.org/10.1007/11844297_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38990-3
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