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Random search optimization based on genetic algorithm and discriminant functionThe general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a genetic algorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.
Document ID
19940004694
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Kiciman, M. O.
(Turkish Aerospace Industries Ankara, Turkey)
Akgul, M.
(Bilkent Univ. Ankara, Turkey)
Erarslanoglu, G.
(Turkish Aerospace Industries Ankara, Turkey)
Date Acquired
August 16, 2013
Publication Date
January 1, 1990
Publication Information
Publication: NASA. Langley Research Center, The Third Air Force(NASA Symposium on Recent Advances in Multidisciplinary Analysis and Optimization
Subject Category
Computer Programming And Software
Accession Number
94N71449
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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