A Hierarchical Genetic System for Symbolic Function Identification
Created by W.Langdon from
gpbibliography.bib Revision:1.6817
 @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
LevenbergMarquardt nonlinear regression algorithm. The
genetic algorithm uses an expressiontree
representation rather than the more usual binarystring
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 LevenbergMarquadt
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
Citations