Created by W.Langdon from gp-bibliography.bib Revision:1.5080

- @InProceedings{nikolaev:2002:oaigpop,
- author = "Nikolay Nikolaev and Lilian M. {de Menezes} and Hitoshi Iba",
- title = "Overfitting Avoidance in Genetic Programming of Polynomials",
- booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002",
- editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton",
- pages = "1209--1214",
- year = "2002",
- publisher = "IEEE Press",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)",
- ISBN = "0-7803-7278-6",
- month = "12-17 " # may,
- notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)",
- keywords = "genetic algorithms, genetic programming, stroganoff, Stroganoff system, complexity tuning, fitness function, genetic programming, local ridge regression, model specification flexibility, overfitting avoidance, polynomial block reformulation, polynomials, predictive models, regularised weight subset selection, search navigation, statistical bias, statistical variance, time series forecasting, forecasting theory, mathematics computing, polynomials, programming, statistical analysis, time series",
- DOI = "doi:10.1109/CEC.2002.1004415",
- abstract = "This paper proposes several techniques for avoiding over fitting in the genetic programming (GP) of polynomials. The model specification flexibility is increased by: (1) a polynomial block reformulation, which reduces the statistical bias, and, (2) complexity tuning using local ridge regression and regularised weight subset selection, which reduce the statistical variance. Another contribution is the designed fitness function for search navigation towards highly predictive models. Experimental results on time-series forecasting show that these techniques help GP to find accurate, less complex and better forecasting polynomials than traditional Koza-style GP (J.R. Koza, 1992) and the previous Stroganoff system (H. Iba et al., 1994, 2001)",
- }

Genetic Programming entries for Nikolay Nikolaev Lilian M de Menezes Hitoshi Iba