Evolutionary Algorithms for the Design of Orthogonal Latin Squares Based on Cellular Automata
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gp-bibliography.bib Revision:1.8081
- @InProceedings{Mariot:2017:GECCO,
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author = "Luca Mariot and Stjepan Picek and
Domagoj Jakobovic and Alberto Leporati",
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title = "Evolutionary Algorithms for the Design of Orthogonal
Latin Squares Based on Cellular Automata",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4920-8",
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address = "Berlin, Germany",
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pages = "306--313",
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size = "8 pages",
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URL = "http://doi.acm.org/10.1145/3071178.3071284",
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DOI = "doi:10.1145/3071178.3071284",
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acmid = "3071284",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, boolean
functions, cellular automata, nonlinearity, orthogonal
latin squares, pairwise balancedness, quaternary
strings",
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month = "15-19 " # jul,
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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.",
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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