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Author Boschetti, F. ♦ Dentith, M. ♦ List, R.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1995
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Genetic algorithms ♦ Testing ♦ Mathematics ♦ Optimization methods ♦ Geophysics computing ♦ Geology ♦ Electronic mail ♦ Geometry ♦ Image processing ♦ Pattern recognition
Abstract GA performance in high-dimensional optimisation problems can be enhanced by the use of a 'pseudo subspace' technique. The method works by projecting the parameter space onto a lower dimensional subspace in the first stages of the optimisation process, in order to allow the GA search to discover the most promising area of the solution space. Subsequently, the dimensionality of the model is progressively increased until a predetermined limit is reached. Comparison between the pseudo-subspace procedure and a conventional GA, using two different GA implementations, shows the former to be more successful when applied to two geophysical problems characterised by different solution-space geometry and mathematics. This technique could be easily transferred to different image processing or pattern recognition problems where geometrical relationships between the parameters are maintained.
Description Author affiliation: Dept. of Geol. & Geophys., Western Australia Univ., Nedlands, WA, Australia (Boschetti, F.; Dentith, M.)
ISBN 0780327594
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1995-11-29
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 460.45 kB
Page Count 4
Starting Page 557
Ending Page 560

Source: IEEE Xplore Digital Library