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Journal of Automation and Information Sciences

Erscheint 12 Ausgaben pro Jahr

ISSN Druckformat: 1064-2315

ISSN Online: 2163-9337

SJR: 0.173 SNIP: 0.588 CiteScore™:: 2

Indexed in

Analysis of Variables and Parameters of Genetic Algorithms for Production Process Planning

Volumen 36, Ausgabe 2, 2004, pp. 51-56
DOI: 10.1615/JAutomatInfScien.v36.i2.50
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ABSTRAKT

The influence of main parameters and variables of genetic algorithms on efficient solution of optimization problem is considered. The data on adaptive organization of parameter selection are presented. Experimental efficiency estimates of genetic algorithms applied to problems of production process planning are considered.

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