Evolving universal hash functions using genetic algorithms

In this paper we explore the use of metaheuristic functions, namely Genetic Algorithms to construct Universal Hash Functions to efficiently hash a given set of keys. The Hash Functions generated in this way should give lesser number of collisions as compared to selecting them randomly from a family of Universal Hash Functions. Simulations and tests performed using this technique provide promising results. The algorithm can be used in scenarios where the input distribution of keys is changing frequently and the hash function needs to be modified often to rehash the values to reduce collisions.