- 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
",
