Evolutionary Algorithms for the Design of Orthogonal Latin Squares Based on Cellular Automata

Created by W.Langdon from gp-bibliography.bib Revision:1.4348

@InProceedings{Mariot:2017:GECCO,
  author =       "Luca Mariot and Stjepan Picek and 
                 Domagoj Jakobovic and Alberto Leporati",
  title =        "Evolutionary Algorithms for the Design of Orthogonal
                 Latin Squares Based on Cellular Automata",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "306--313",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071284",
  DOI =          "doi:10.1145/3071178.3071284",
  acmid =        "3071284",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, boolean
                 functions, cellular automata, nonlinearity, orthogonal
                 latin squares, pairwise balancedness, quaternary
                 strings",
  month =        "15-19 " # jul,
  abstract =     "We investigate the design of Orthogonal Latin Squares
                 (OLS) by means of Genetic Algorithms (GA) and Genetic
                 Programming (GP). Since we focus on Latin squares
                 generated by Cellular Automata (CA), the problem can be
                 reduced to the search of pairs of Boolean functions
                 that give rise to OLS when used as CA local rules. As
                 it is already known how to design CA-based OLS with
                 linear Boolean functions, we adopt the evolutionary
                 approach to address the nonlinear case, experimenting
                 with different encodings for the candidate solutions.
                 In particular, for GA we consider single bitstring,
                 double bitstring and quaternary string encodings, while
                 for GP we adopt a double tree representation. We test
                 the two metaheuristics on the spaces of local rules
                 pairs with n = 7 and n = 8 variables, using two fitness
                 functions. The results show that GP is always able to
                 generate OLS, even if the optimal solutions found with
                 the first fitness function are mostly linear. On the
                 other hand, GA achieves a remarkably lower success rate
                 than GP in evolving OLS, but the corresponding Boolean
                 functions are always nonlinear.",
  notes =        "Also known as \cite{Mariot:2017:EAD:3071178.3071284}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Luca Mariot Stjepan Picek Domagoj Jakobovic Alberto Leporati

Citations