Abstract
In the current paper we present an integrated genetic programming environment with a graphical user interface (GUI), called jGPModeling. The jGPModeling environment was developed using the JAVA programming language, and is an implementation of the steady-state genetic programming algorithm. That algorithm evolves tree based structures that represent models of input – output relation of a system. During the design and implementation of the application, we focused on the execution time optimization and tried to limit the bloat effect. In order to evaluate the performance of the jGPModeling environment, two different real world system modeling tasks were used.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Koza, J.R.: Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems Technical Report STAN-TR-CS 1314, Stanford University (1990)
Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)
Koza, J.R.: Genetic Programming. In: Williams, J.G., Kent, A. (eds.) Encyclopedia of Computer Science and Technology, vol. 39 (suppl. 24), pp. 29–43. Marcel Dekker, New York (1998)
Dassios, G., Fokas, A.S., Hadjiloizi, D.: On the complementarity of EEG and MEG. Inverse Problems 23, 2541–2549 (2007)
Anninos, P.A., Tsagas, N., Adamopoulos, A.: A brain model theory for epilepsy and its treatment: experimental verification using SQUID measurements. In: Cotterill, R.M.J. (ed.) Models of brain function, pp. 405–422. Cambridge University Press, Cambridge (1989)
Jouny, C.C., Frabaszczuk, P.L., Bergey, G.K.: Complexity-based analysis of the dynamic of propagation of epileptic seizures. In: Proceedings of the 2nd International IEEE EMBS Conference on Neural Engineering, pp. 155–157 (2005)
Tsoutsouras, V.G., Iliopoulos, A.C., Pavlos, G.P.: Non-linear analysis and modelling of EEG in patients with epilepsy. In: 19th Panhellenic Conference on Nonlinear Science and Complexity, Thessaloniki, Greece, July 10-22 (2006)
Adamopoulos, A., Pavlos, G., Anninos, P., Tsitsis, D., Rigas, A.: Chaotic analysis of biomagnetic measurements of human brain, NATO ASI on Chaotic Synamics – Theory and Practice, Patras, Greece, July, 11-20 (1991)
Adamopoulos, A.V.: Intelligent adaptive modelling and identification of MEG of epileptic patients. WSEAS Transactions on Biology and Biomedicine 3(2), 69–76 (2006)
Philippidis, T.P., Vassilopoulos, A.P.: Complex stress state effect on fatigue life of GRP laminates. Part I, experimental. Int. J. Fatigue 24, 813–823 (2002)
Nijssen, R.P.L.: OptiDAT – fatigue of wind turbine materials database, http://www.kc-wmc.nl/optimat_blades/index.htm
Winkler, S.: Identifying Nonlinear Model Structures Using Genetic Programming. Diploma Thesis, Institute of Systems Theory and Simulation, Johannes Kepler University Linz, Austria (2004)
Winkler, S., Affenzeller, M., Wagner, S.: New Methods for the Identification of Nonlinear Model Structures Based Upon Genetic Programming Techniques. In: Proceedings of the 15th International Conference on Systems Science, vol. 1, pp. 386–393. Oficyna Wydawnicza Politechniki Wroclawskiej (2004)
Winkler, S., Affenzeller, M., Wagner, S.: Identifying Nonlinear Model Structures Using Genetic Programming Techniques. In: Cybernetics and Systems 2004, pp. 689–694. Austrian Society for Cybernetic Studies (2004)
Madár, J., Abonyi, F.: Szeifert: Genetic Programming for the Identification of Nonlinear Input-Output Models (accepted, 2005)
Madár, J., Abonyi, J., Szeifert, F.: Genetic Programming for System Identification. In: Intelligent Systems Design and Applications (ISDA) (2004)
Willis, M.J., Hiden, H.G., Marenbach, P., McKay, B., Montague, G.A.: Genetic programming: An introduction and survey of applications. In: Second International Conference on Genetic Algorithms in Engineering Systems, pp. 314–319 (1997)
Tsang, E.P.K., Butler, J.M., Li, J.: EDDIE Beats the Bookies. Journal of SOFTWARE – Practice and Experience 28(10) (1998)
Alex, F., Stechert, A.: Evolving Nonlinear Predictive Models for Lossless Image Compression with Genetic Programming. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 95–102. Morgan Kaufmann, Wisconsin (1998)
Werner, J.C., Fogarty, T.C.: Genetic programming applied to Collagen disease & thrombosis. South Bank University, London (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Georgopoulos, E.F., Zarogiannis, G.P., Adamopoulos, A.V., Vassilopoulos, A.P., Likothanassis, S.D. (2008). A Genetic Programming Environment for System Modeling. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2008. Lecture Notes in Computer Science(), vol 5138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87881-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-87881-0_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87880-3
Online ISBN: 978-3-540-87881-0
eBook Packages: Computer ScienceComputer Science (R0)