- author = "Michael Kommenda and Gabriel Kronberger and Stephan Winkler and Michael Affenzeller and Stefan Wagner",
- title = "Effects of constant optimization by nonlinear least squares minimization in symbolic regression",
- booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion",
- year = "2013",
- editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks",
- isbn13 = "978-1-4503-1964-5",
- keywords = "genetic algorithms, genetic programming",
- pages = "1121--1128",
- month = "6-10 " # jul,
- organisation = "SIGEVO",
- address = "Amsterdam, The Netherlands",
-
DOI = "
doi:10.1145/2464576.2482691",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "In this publication a constant optimisation approach
for symbolic regression is introduced to separate the
task of finding the correct model structure from the
necessity to evolve the correct numerical constants. A
gradient-based nonlinear least squares optimisation
algorithm, the Levenberg-Marquardt (LM) algorithm, is
used for adjusting constant values in symbolic
expression trees during their evolution. The LM
algorithm depends on gradient information consisting of
partial derivations of the trees, which are obtained by
automatic differentiation.
The presented constant optimization approach is tested on several benchmark problems and compared to a standard genetic programming algorithm to show its effectiveness. Although the constant optimization involves an overhead regarding the execution time, the achieved accuracy increases significantly as well as the ability of genetic programming to learn from provided data. As an example, the Pagie-1 problem could be solved in 37 out of 50 test runs, whereas without constant optimisation it was solved in only 10 runs. Furthermore, different configurations of the constant optimisation approach (number of iterations, probability of applying constant optimisation) are evaluated and their impact is detailed in the results section.",
- notes = "Also known as \cite{2482691} Distributed at GECCO-2013.",
