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Multi-objective Evolution of Ultra-Fast General-Purpose Hash Functions

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Genetic Programming (EuroGP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10781))

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Abstract

Hashing is an important function in many applications such as hash tables, caches and Bloom filters. In past, genetic programming was applied to evolve application-specific as well as general-purpose hash functions, where the main design target was the quality of hashing. As hash functions are frequently called in various time-critical applications, it is important to optimize their implementation with respect to the execution time. In this paper, linear genetic programming is combined with NSGA-II algorithm in order to obtain general-purpose, ultra-fast and high-quality hash functions. Evolved hash functions show highly competitive quality of hashing, but significantly reduced execution time in comparison with the state of the art hash functions available in literature.

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Acknowledgments

This work was supported by the Czech science foundation project 16-08565S. The authors would like to thank Dr. Martin Zadnik for his valuable comments to this research.

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Authors and Affiliations

  1. IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, Božetěchova 2, 612 66, Brno, Czech Republic

    David Grochol & Lukas Sekanina

Authors
  1. David Grochol
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  2. Lukas Sekanina
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Corresponding author

Correspondence to David Grochol .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Lisbon, Portugal

    Mauro Castelli

  2. Brno University of Technology, Brno, Czech Republic

    Lukas Sekanina

  3. Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. University of Parma, Parma, Italy

    Stefano Cagnoni

  5. University of Cádiz, Cádiz, Spain

    Pablo García-Sánchez

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Grochol, D., Sekanina, L. (2018). Multi-objective Evolution of Ultra-Fast General-Purpose Hash Functions. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science(), vol 10781. Springer, Cham. https://doi.org/10.1007/978-3-319-77553-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-77553-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77552-4

  • Online ISBN: 978-3-319-77553-1

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