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https://doi.org/10.2498/cit.1001419

Evolutionary Synthesis of Cellular Automata

Jernej Zupanc ; University of Ljubljana, Slovenia
Bogdan Filipič ; Jozef Stefan Institute, Slovenia

Puni tekst: engleski, pdf (232 KB) str. 105-112 preuzimanja: 521* citiraj
APA 6th Edition
Zupanc, J. i Filipič, B. (2011). Evolutionary Synthesis of Cellular Automata. Journal of computing and information technology, 19 (2), 105-112. https://doi.org/10.2498/cit.1001419
MLA 8th Edition
Zupanc, Jernej i Bogdan Filipič. "Evolutionary Synthesis of Cellular Automata." Journal of computing and information technology, vol. 19, br. 2, 2011, str. 105-112. https://doi.org/10.2498/cit.1001419. Citirano 27.02.2020.
Chicago 17th Edition
Zupanc, Jernej i Bogdan Filipič. "Evolutionary Synthesis of Cellular Automata." Journal of computing and information technology 19, br. 2 (2011): 105-112. https://doi.org/10.2498/cit.1001419
Harvard
Zupanc, J., i Filipič, B. (2011). 'Evolutionary Synthesis of Cellular Automata', Journal of computing and information technology, 19(2), str. 105-112. https://doi.org/10.2498/cit.1001419
Vancouver
Zupanc J, Filipič B. Evolutionary Synthesis of Cellular Automata. Journal of computing and information technology [Internet]. 2011 [pristupljeno 27.02.2020.];19(2):105-112. https://doi.org/10.2498/cit.1001419
IEEE
J. Zupanc i B. Filipič, "Evolutionary Synthesis of Cellular Automata", Journal of computing and information technology, vol.19, br. 2, str. 105-112, 2011. [Online]. https://doi.org/10.2498/cit.1001419

Sažetak
Synthesis of cellular automata is an important area of modeling and describing complex systems. Large amounts of combinations and candidate solutions render the usage of deterministic approaches impractical and thus nondeterministic optimization methods have to be employed. Two of the typical evolutionary approaches to synthesizing cellular automata are the evolution of a single automaton and a genetic algorithm that evolves a population of automata. The first approach, with addition of some heuristics, is known as the cellular programming algorithm. In this paper we address the second approach and develop a genetic algorithm that evolves a population of cellular automata. We test both approaches on the density classification task, which is one of the most widely studied computational problems in the context of evolving cellular automata. Comparison of the synthesized cellular automata demonstrates unexpected similarity of the evolved rules and comparable classification accuracy performance of both approaches.

Ključne riječi
cellular automata; cellular programming algorithm; density classification task; evolutionary computing; genetic algorithm

Hrčak ID: 71045

URI
https://hrcak.srce.hr/71045

Posjeta: 777 *