- author = "Michael F. Korns",
- title = "Symbolic regression using abstract expression grammars",
- booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation",
- year = "2009",
- editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding",
- bibsource = "DBLP, http://dblp.uni-trier.de",
- pages = "859--862",
- address = "Shanghai, China",
- organisation = "SigEvo",
-
DOI = "
doi:10.1145/1543834.1543960",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = jun # " 12-14",
- isbn13 = "978-1-60558-326-6",
- keywords = "genetic algorithms, genetic programming, Poster, PSO, DE",
- abstract = "Expression Grammars have the potential to integrate Genetic Algorithms, Genetic Programming, Swarm Intelligence, and Differential Evolution into a seamlessly unified array of tools for use in symbolic regression. The features of abstract expression grammars are explored, examples of implementations are provided, and the beneficial effects of abstract expression grammars are tested with several published nonlinear regression problems.",
-
notes = "p 860 {"}Abstract constants represent placeholders for
real numbers which are to be optimized{"}. Abstract
features inputs again optimised by PSO, DE, etc.
Abstract functions chosen by GA etc from predefined
list of functions. User uses grammar to decide how much
freedom or how constrained system is to be. 10000
training examples.
Also known as \cite{DBLP:conf/gecco/Korns09}. Part of \cite{DBLP:conf/gec/2009}",
