Variables diversity in systems identification based on extended genetic programming

• Autorzy: Winkler S. , Affenzeller M. , Wagner S.
• Języki publikacji: EN

Abstrakty

EN

There are several methods that are frequently used for solving data based system identification problems; genetic programming (GP) has already been used successfully for solving data mining problems in the context of several scientific domains. Extended functional bases, additional optimization phases and further developed selection mechanisms essentially contribute to the method's ability to generate high quality results for various kinds of data based identification scenarios. Even though there has already been a lot of investigation regarding the optimization of the method and its parameter settings, there is still rather little systematic analysis of internal processes regarding genetic dynamics and the progress of genetic diversity during the execution of genetic programming based identification using these algorithmic extensions. In this paper, we report on results of investigations regarding exactly these aspects: We have developed methods and statistical features that are able to describe genetic diversity and dynamics of GP-based structure identification algorithms; here, we introduce statistic analysis of genetic diversity regarding variables and time offset settings within GP populations. Genetic diversity is (amongst other aspects) characterized by the occurrence of variables for the models in which they are used; statistical methods for estimating respective impact features are also presented here. Data sets representing two different kinds of systems (complex mechatronical systems as well as medical benchmark data) have been used for empirical tests; furthermore, standard implementations of genetic programming are compared to extended techniques including offspring selection as well as sliding window techniques.

Słowa kluczowe

EN

systems identification; genetic programming; genetic diversity

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2008
• Tom: Vol. 34, no 2
• Strony: 35--41
• Opis fizyczny: Bibliogr. 13 poz., wykr.

Twórcy

autor

Winkler S.

autor

Affenzeller M.

autor

Wagner S.

• Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communications and Media, Upper Austria University of Applied Sciences, Softwarepark 11, 4232 Hagenberg, Austria,

Bibliografia

• [1] Affenzeller M., Population Genetics and Evolutionary Computation: Theoretical and Practical Aspects, Schriften der Johannes Kepler Universität Linz, Universitätsverlag Rudolf Trauner, ISBN 3-85487-823-0, 2005.
• [2] Affenzeller M., Wagner S., Offspring Selection: A New Self-Adaptive Selection Scheme for Genetic Algorithms, Adaptive and Natural Computing Algorithms, Springer Computer Science, 2005, pp. 218-221.
• [3] Alberer D., Del Re L., Winkler S., Langthaler P., Virtual Sensor Design of Paniculate and Nitric Oxide Emissions in a DI Diesel Engine, Proc. 7th Int. Conf. Engines for Automobile ICE 2005, Capri, Napoli, 2005, paper no. 2005-24-063.
• [4] Draper N.R., Smith H., Applied Regression Analysis, Wiley, New York 1998.
• [5] Goldberg D.E., Genetic Algorithms for Search, Optimization, and Machine Learning, Addison-Wesley, Reading, 1989.
• [6] Holland J.H., Adaptation in Natural Artificial Systems, University of Michigan Press, Ann Arbor, 1975.
• [7] Koza J.R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press, ISBN 0-262-11170-5, 1992.
• [8] Langdon B., Poli R., Foundations of Genetic Programming, Springer, 2002.
• [9] Wagner S., Affenzeller M., HeuristicLab: A Generic and Extensible Optimization Environment, Adaptive and Natural Computing Algorithms, Springer Computer Science, 2005, pp. 538-541.
• [10] Winkler S., Affenzeller M., Wagner S., New Methods for the Identification of Nonlinear Model Structures Based Upon Genetic Programming Techniques, Journal of Systems Science, Oficyna Wydawnicza Politechniki Wrocławskiej, Vol. 31, 2005, pp. 5-13.
• [11] Winkler S., Affenzeller M., Wagner S., Advances in Applying Genetic Programming to Machine Learning, Focussing on Classification Problems, Proc. 20th IEEE Int. Parallel & Distributed Processing Symposium IPDPS 2006, IEEE Catalog Number 06TH8860, paper nr. NIDISC-012.
• [12] Winkler S., Efendic H., Del Re L., Quality Pre-Assessment in Steel Industry Using Data Based Estimators, Proc. IFAC Workshop MMM’2006 on Automation in Mining, Mineral and Metal Industry, International Federation for Automatic Control (IFAC) 2006, pp. 185-190.
• [13] Winkler S., Affenzeller M., Wagner S., Advanced Genetic Programming Based Machine Learning, Journal of Mathematical Modelling and Algorithms, ISSN 1570-1166 (print), 1572-9214 (online), DOI 10.1007/s10852-007-9065-6, Springer Netherlands, 2007.
• [14] Winkler S., Affenzeller M., WAGNER S., Selection Pressure Driven Sliding Window Genetic Programming, Computer Aided Systems Theory - EUROCAST 2007, Lecture Notes in Computer Science 4739, Springer, 2007, pp. 788-795.