Genetic programming with either stochastic or deterministic constant evaluation

Vladimír Hlaváč

Abstract


Constant evaluation is a key problem for symbolic regression, one solved
by means of genetic programming. For constant evaluation, other evolutionary methods are often used. Typical examples are some variants of genetic programming or evolutionary systems, all of which are stochastic. The article compares these methods with a deterministic approach using exponentiated gradient descent. All the methods were tested on single sample function to maintain the same conditions and results are presented in graphs. Finally, three different tasks (ten times each) are compared to check the reliability of the methods tested in the article.

Keywords


Genetic programming, Constant evaluation, Gradient descent, Symbolic regrassion

References


BARMPALEXISA, P., K. KACHRIMANISA, A. TSAKONASB, E. GEORGARAKIS: Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation. in: Chemometrics and Intelligent Laboratory Systems. Volume 107, Issue 1, May 2011, pp. 75-82 (Elsevier)

BRANDEJSKY T.: Genetic Programming Algorithm with constants pre-optimization of modified candidates of new population. In: Mendel 2004, Brno, 2004, pp. 34-38.

BRANDEJSKY, T.: Evolutionary system to model structure and parameters regression. In: Neural Network World, 2012, 22(2), pp. 181-194. ISSN 1210-0552, doi: 10.14311/NNW.2012.22.01.

BRANDEJSKY, T.: Small populations in GPA-ES algorithm. In: Mendel 2013, pp. 31-36. ISBN: 978-802144755-4.

BRANDEJSKY, T.: Influence of (p)RNGs onto GPA-ES behaviors. In: Neural Network World, 2017, 27(6), pp. 593-605. ISSN 1210-0552, doi: 10.14311/NNW.2017.27.03.

HANSEN, N., D.V. ARNOLD, A. AUGER: Evolution Strategies. In: Janusz Kacprzyk and Witold Pedrycz (Eds.): Handbook of Computational Intelligence, Springer, Chapter 44, pp.871-898 (2015) (pdf available from: https://www.lri.fr/~{}hansen/es-overview-2015.pdf).

HLAVAC, V.: A program searching for a functional dependence using genetic programming with coefficient adjustment. In: 2016 Smart Cities Symposium Prague (SCSP), 2016.

KIRKPATRICK, S., GELATT JR., C.D., VECCHI, M.P.: Optimization by simulated annealing. Science, 220 (4598), pp. 671-680. (1983)

KIVINEN, J., WARMUTH, M. K.: Exponentiated gradient versus gradient descent for linear predictors. In: Information and Computation, 132(1):1{63, Elsevier, 1997. Available from: http://www.sciencedirect.com/science/article/pii/S0890540196926127. doi: 10.1006/inco.1996.261" target="_blank">10.1006/inco.1996.261" target="_blank">http://www.sciencedirect.com/science/article/pii/S0890540196926127. doi: 10.1006/inco.1996.261.

KOZA, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA, MIT Press, 1992. ISBN 978-0262111706.

LAHODIUK, Y.: Genetic-programming. GitHub, Inc., Available from: https://github.com/lagodiuk/genetic-programming (published 2014, cited 17.1.2017.

MITCHELL, MELANIE: An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press (1996). ISBN 9780585030944

POLI, R., LANGDON, W.B., MCPHEE, N.F., KOZA, J. R.: A Field Guide to Genetic Programming. Lulu.com, 2008.

WEISSER R., O LMERA P., MATOU LEK R.: Transplant Evolution with Modified Schema of Differential Evolution : Optimization Structure of Controllers. In: International Conference on Soft Computing MENDEL. Brno : MENDEL, 2010.

ZELINKA I.: Analytic Programming by Means of Soma Algorithm. In: Proc. 8th International Conference on Soft Computing Mendel'02, Brno, Czech Republic, 2002, pp. 93-101., ISBN 80-214-2135-5

ZONGKER, D., PUNCH, B., RAND, B.: Lil-gp Genetic Programming System. Michigan State University, 1996.




DOI: http://dx.doi.org/10.14311/NNW.2018.%25x

Refbacks

  • There are currently no refbacks.


Should you encounter an error (non-functional link, missing or misleading information, application crash), please let us know at nnw.ojs@fd.cvut.cz.
Please, do not use the above address for non-OJS-related queries (manuscript status, etc.).
For your convenience we maintain a list of frequently asked questions here. General queries to items not covered by this FAQ shall be directed to the journal editoral office at nnw@fd.cvut.cz.