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Original scientific paper
https://doi.org/10.2498/cit.2002.04.01

Dynamic Systems Modeling with Stochastic Cellular Automata (Evolutionary versus Stochastic Correlation Approach)

Andrej Likar
Simon Vavpotič
Andrej Dobnikar

Fulltext: english, pdf (628 KB) pages 251-259 downloads: 1.238* cite
APA 6th Edition
Likar, A., Vavpotič, S. & Dobnikar, A. (2002). Dynamic Systems Modeling with Stochastic Cellular Automata (Evolutionary versus Stochastic Correlation Approach). Journal of computing and information technology, 10 (4), 251-259. https://doi.org/10.2498/cit.2002.04.01
MLA 8th Edition
Likar, Andrej, et al. "Dynamic Systems Modeling with Stochastic Cellular Automata (Evolutionary versus Stochastic Correlation Approach)." Journal of computing and information technology, vol. 10, no. 4, 2002, pp. 251-259. https://doi.org/10.2498/cit.2002.04.01. Accessed 4 Apr. 2020.
Chicago 17th Edition
Likar, Andrej, Simon Vavpotič and Andrej Dobnikar. "Dynamic Systems Modeling with Stochastic Cellular Automata (Evolutionary versus Stochastic Correlation Approach)." Journal of computing and information technology 10, no. 4 (2002): 251-259. https://doi.org/10.2498/cit.2002.04.01
Harvard
Likar, A., Vavpotič, S., and Dobnikar, A. (2002). 'Dynamic Systems Modeling with Stochastic Cellular Automata (Evolutionary versus Stochastic Correlation Approach)', Journal of computing and information technology, 10(4), pp. 251-259. https://doi.org/10.2498/cit.2002.04.01
Vancouver
Likar A, Vavpotič S, Dobnikar A. Dynamic Systems Modeling with Stochastic Cellular Automata (Evolutionary versus Stochastic Correlation Approach). Journal of computing and information technology [Internet]. 2002 [cited 2020 April 04];10(4):251-259. https://doi.org/10.2498/cit.2002.04.01
IEEE
A. Likar, S. Vavpotič and A. Dobnikar, "Dynamic Systems Modeling with Stochastic Cellular Automata (Evolutionary versus Stochastic Correlation Approach)", Journal of computing and information technology, vol.10, no. 4, pp. 251-259, 2002. [Online]. https://doi.org/10.2498/cit.2002.04.01

Abstracts
A new approach to dynamic systems modeling is given. Stochastic Cellular Automata (SCA) are used as the basic computational module. The dynamic systems are considered as time and space dependent, where time dependencies are supposed to be given with some differential equations (DE), while space influences are not known. The basic idea of our approach is to use heuristics for the design of SCA and some stochastic search algorithm to optimize free model parameters. Two non-gradient optimization algorithms are used and evaluated on the two case studies: diffusion and migration of Cs in soil and forest fire spread problem. They are Evolutionary Algorithm (EA) and Stochastic Correlation Algorithm (ALOPEX). We show that with some modifications, both algorithms are capable to solve the two case problems, though there are some important differences between them.

Hrčak ID: 44768

URI
https://hrcak.srce.hr/44768

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