The strict avalanche criterion: spectral properties of boolean functions and an extended definition
CRYPTO '88 Proceedings on Advances in cryptology
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Evolutionary Design of Hashing Function Circuits Using an FPGA
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
An Experimental Study on Fitness Distributions of Tree Shapes in GP with One-Point Crossover
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
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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.