Created by W.Langdon from gp-bibliography.bib Revision:1.3989

@InProceedings{Jiang:1992:hGPsfi, author = "Mingda Jiang and Alden H. Wright", title = "A Hierarchical Genetic System for Symbolic Function Identification", institution = "University of Montana, Missoula, MT 59812", booktitle = "Proceedings of the 24th Symposium on the Interface: Computing Science and Statistics, College Station, Texas", year = "1992", month = mar, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.umt.edu/u/wright/papers/hgsfi.ps.gz", URL = "http://citeseer.ist.psu.edu/202012.html", size = "27 pages", abstract = "Given data in the form of a collection of (x,y) pairs of real numbers, the symbolic function identification problem is to find a functional model of the form y = f(x) that fits the data. This paper describes a system for solution of symbolic function identification problems that combines a genetic algorithm and the Levenberg-Marquardt nonlinear regression algorithm. The genetic algorithm uses an expression-tree representation rather than the more usual binary-string representation. Experiments were run with data generated using a wide variety of function models. The system was able to find a function model that closely approximated the data with a very high success rate.", notes = "Also available as technical report, 26 pages. Does Symbolic regression but uses Levenberg-Marquadt statistical technique to adjust parameters to get closer (equivalent of local hill climbing?) Some case GP don't work on. Mentions Permutation but don't say how usefully it is ", }

Genetic Programming entries for Mingda Jiang Alden H Wright