Mixed IFS: resolution of the inverse problem using genetic programming
Inhaltsverzeichnis
Reference
E. Lutton, J. Lévy Véhel, G. Cretin, P. Glevarec, and C. Roll: Mixed IFS: resolution of the inverse problem using genetic programming. Complex Systems, 9:375–398, 1995.
DOI
Abstract
We address here th e resolution of t he so-called inverse problem for the iterated functions syste m (IFS). This problem has al- ready been widely considered, and some studies have been performed for t he affine IFS , using determin istic or sto chast ic met hods (simu- lated annealing or genetic algorit hm). In dealing with the nonaffine IFS, the usual techniques do not perform well unless some a priori hypoth eses on th e struct ure of the IFS (number and typ e of funct ions) are made. In thi s work, a genetic programming method is investigated to solve the "general" inverse probl em, which allows t he simult aneous perform ance of a numeric and a symbolic optimization . The use of a "mixed IFS" may enlarge th e scope of some applications, for example, image compression, because it allows a wider range of shapes to be coded.
Extended Abstract
Bibtex
Used References
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Sonstige Links
auch in: Artificial Evolution, Lecture Notes in Computer Science Volume 1063, 1996, pp 245-258 http://link.springer.com/chapter/10.1007%2F3-540-61108-8_42
