Abstract
This paper conducts an investigation into the manner in which constants evolve during the course of GP run. It starts by describing an intuitive Gaussian type mutation for constants and showing that its ability to produce small changes in individuals leads to a high performance. It then demonstrates the surprising result that, in a selection of real world problems, simple random mutation performs better. The paper then finishes with an analysis of the diversity of constants in the population, and the manner in which this changes over time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Les M. Howard and Donna J. D’Angelo, The GA-P: A genetic algorithm and genetic programming hybrid, IEEE Expert 10 (1995), no. 3, 11–15.
Maarten Keijzer and Vladan Babovic, Genetic programming, ensemble methods and the bias/variance tradeoff-introductory investigations, Genetic Programming, Proceedings of EuroGP’2000 (Edinburgh) (Riccardo Poli, Wolfgang Banzhaf, William B. Langdon, Julian F. Miller, Peter Nordin, and Terence C. Fogarty, eds.), LNCS, vol. 1802, Springer-Verlag, 15–16 April 2000, pp. 76–90.
Mike J. Keith and Martin C. Martin, Genetic programming in C++: Implementation issues, Advances in Genetic Programming (Kenneth E. Kinnear, Jr., ed.), MIT Press, 1994, pp. 285–310.
John R. Koza, Genetic programming: On the programming of computers by means of natural selection, MIT Press, Cambridge, MA, USA, 1992.
Nicholas Freitag McPhee and Nicholas J. Hopper, Analysis of genetic diversity through population history, Proceedings of the Genetic and Evolutionary Computation Conference (Orlando, Florida, USA) (Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, eds.), vol. 2, Morgan Kaufmann, 13–17 July 1999, pp. 1112–1120.
Marc Schoenauer, Michele Sebag, Francois Jouve, Bertrand Lamy, and Habibou Maitournam, Evolutionary identification of macro-mechanical models, Advances in Genetic Programming 2 (Peter J. Angeline and K. E. Kinnear, Jr., eds.), MIT Press, Cambridge, MA, USA, 1996, pp. 467–488.
Alexander Topchy and W. F. Punch, Faster genetic programming based on local gradient search of numeric leaf values, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001) (San Francisco, California, USA) (Lee Spector, Erik D. Goodman, Annie Wu, W. B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, eds.), Morgan Kaufmann, 7–11 July 2001, pp. 155–162.
Vassili V. Toropov and Luis F. Alvarez, Application of genetic programming to the choice of a structure of multipoint approximations, 1st ISSMO/NASA Internet Conf. on Approximations and Fast Reanalysis in Engineering Optimization, June 14–27 1998, Published on a CD ROM.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ryan, C., Keijzer, M. (2003). An Analysis of Diversity of Constants of Genetic Programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_38
Download citation
DOI: https://doi.org/10.1007/3-540-36599-0_38
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00971-9
Online ISBN: 978-3-540-36599-0
eBook Packages: Springer Book Archive