Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/wcecs_2009_II/193, title = "Grammatical Evolution: STE criterion in Symbolic Regression Task", author = "R. Matousek", booktitle = "Proceedings of the World Congress on Engineering and Computer Science, WCECS '09", year = "2009", editor = "S. I. Ao and Craig Douglas and W. S. Grundfest and Jon Burgstone", volume = "II", pages = "1050--1054", address = "San Francisco, USA", month = oct # " 20-22", publisher = "Newswood Limited", organization = "International Association of Engineers", keywords = "genetic algorithms, genetic programming, grammatical evolution, Grammatical Evolution, SSE, STE, Epsilon Tube, Laplace Distribution", isbn13 = "978-988-18210-2-7", URL = "http://www.iaeng.org/publication/WCECS2009/WCECS2009_pp1050-1054.pdf", size = "5 pages", abstract = "Grammatical evolution (GE) is one of the newest among computational methods (Ryan et al., 1998 \cite{Ryan:1998:mendle}), (O'Neill and Ryan, 2001 \cite{oneill:2001:TEC}). Basically, it is a tool used to automatically generate Backus-Naur-Form (BNF) computer programs. The method's evolution mechanism may be based on a standard genetic algorithm (GA). GE is very often used to solve the problem of a symbolic regression, determining a module's own parameters (as it is also the case of other optimization problems) as well as the module structure itself. A Sum Square Error (SSE) method is usually used as the testing criterion. In this paper, however, we will present the original method, which uses a Sum Epsilon Tube Error (STE) optimizing criterion. In addition, we will draw a possible parallel between the SSE and STE criteria describing the statistical properties of this new and promising minimizing method.", notes = "fitness like Koza's hits but minimum distance required for a near miss is changed during GP run. Suggests STE fitness follows Laplace (symmetric exponential) [Equation 6 says Binomial?] distribution whilst sum of errors squared follows Gaussian distribution. STE gives smoother fit (fig 3 and fig4). Minitab. 2 one dimensional problems. Lecture Notes in Engineering and Computer Science", }

Genetic Programming entries for Radomil Matousek