Napredna pretraga

Pregled bibliografske jedinice broj: 891466

Evolutionary algorithms for the design of orthogonal latin squares based on cellular automata


Mariot, Luca; Picek, Stjepan; Jakobović, Domagoj; Leporati, Alberto
Evolutionary algorithms for the design of orthogonal latin squares based on cellular automata // Proceedings of the Genetic and Evolutionary Computation Conference Pages
Berlin, Njemačka, 2017. str. 306-313 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Evolutionary algorithms for the design of orthogonal latin squares based on cellular automata

Autori
Mariot, Luca ; Picek, Stjepan ; Jakobović, Domagoj ; Leporati, Alberto

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the Genetic and Evolutionary Computation Conference Pages / - , 2017, 306-313

ISBN
978-1-4503-4920-8

Skup
Genetic and Evolutionary Computation Conference, GECCO 2017

Mjesto i datum
Berlin, Njemačka, 15-19.07.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Orthogonal Latin Squares

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2014-09-4882 - Heuristička optimizacija u kriptologiji (Domagoj Jakobović, )

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb