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
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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