- author = "Maarten Keijzer",
- title = "Improving Symbolic Regression with Interval Arithmetic and Linear Scaling",
- booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
- year = "2003",
- editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa",
- volume = "2610",
- series = "LNCS",
- pages = "70--82",
- address = "Essex",
- publisher_address = "Berlin",
- month = "14-16 " # apr,
- organisation = "EvoNet",
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming, HPG, Interval Arithmetic",
- ISBN = "3-540-00971-X",
-
URL = "
http://www.cs.vu.nl/~mkeijzer/publications/eurogp2003.ps.gz",
-
URL = "
https://rdcu.be/c9fl4",
-
DOI = "
10.1007/3-540-36599-0_7",
- size = "13 pages",
- abstract = "The use of protected operators and squared error measures are standard approaches in symbolic regression. It will be shown that two relatively minor modifications of a symbolic regression system can result in greatly improved predictive performance and reliability of the induced expressions. To achieve this, interval arithmetic and linear scaling are used. An experimental section demonstrates the improvements on 15 symbolic regression problems.",
-
notes = "Cited by \cite{Ni:2012:ieeeTEC}.
EuroGP'2003 held in conjunction with EvoWorkshops 2003",
