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Evolutionary Search of Binary Orthogonal Arrays

  • Luca Mariot
  • Stjepan Picek
  • Domagoj Jakobovic
  • Alberto Leporati
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11101)

Abstract

Orthogonal Arrays (OA) represent an interesting breed of combinatorial designs that finds applications in several domains such as statistics, coding theory, and cryptography. In this work, we address the problem of constructing binary OA through evolutionary algorithms, an approach which received little attention in the combinatorial designs literature. We focus on the representation of a feasible solution, which we encode as a set of Boolean functions whose truth tables are used as the columns of a binary matrix, and on the design of an appropriate fitness function and variation operators for this problem. We finally present experimental results obtained with genetic algorithms (GA) and genetic programming (GP) on optimizing such fitness function, and compare the performances of these two metaheuristics with respect to the size of the considered problem instances. The experimental results show that GP outperforms GA at handling this type of problem, as it converges to an optimal solution in all considered problem instances but one.

Keywords

Orthogonal arrays Genetic algorithms Genetic programming Boolean functions 

Notes

Acknowledgments

This work has been supported in part by Croatian Science Foundation under the project IP-2014-09-4882.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Luca Mariot
    • 1
  • Stjepan Picek
    • 2
  • Domagoj Jakobovic
    • 3
  • Alberto Leporati
    • 1
  1. 1.DISCo, Università degli Studi di Milano-BicoccaMilanoItaly
  2. 2.Cyber Security Research GroupDelft University of TechnologyDelftThe Netherlands
  3. 3.Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia

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