Grammatical Evolution: STE criterion in Symbolic Regression Task

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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

Citations