Abstract
In this chapter we discuss sliding window symbolic regression and its ability to systematically detect changing dynamics in data streams. The sliding window defines the portion of the data visible to the algorithm during training and is moved over the data. The window is moved regularly based on the generations or on the current selection pressure when using offspring selection. The sliding window technique has the effect that population has to adapt to the constantly changing environmental conditions.
In the empirical section of this chapter, we focus on detecting change points of analyzed systems’ dynamics. We show its effectiveness on various artificial data sets and discuss the results obtained when the sliding window moved in each generation and when it is moved only when a selection pressure threshold is reached. The results show that sliding window symbolic regression can be used to detect change points in systems dynamics for the considered data sets.
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Affenzeller M, Winkler S, Wagner S, Beham A (2009) Genetic algorithms and genetic programming: modern concepts and practical applications. Numerical insights. CRC Press, Singapore
Affenzeller M, Winkler SM, Kronberger G, Kommenda M, Burlacu B, Wagner S (2013) Gaining deeper insights in symbolic regression. In: Riolo R, Moore JH, Kotanchek M (eds) Genetic programming theory and practice XI, genetic and evolutionary computation. Springer, Ann Arbor, chap 10, pp 175–190
Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 97–106
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge
Poli R (2003) A simple but theoretically-motivated method to control bloat in genetic programming. In: Ryan C, Soule T, Keijzer M, Tsang E, Poli R, Costa E (eds) Genetic programming, Proceedings of EuroGP'2003. Springer-Verlag, Essex, LNCS, vol 2610, pp 204–217
Rasmussen CE, Williams CK (2006) Gaussian processes for machine learning. The MIT Press, Cambridge, Massachusetts
Vladislavleva K, Veeramachaneni K, Burland M, Parcon J, O’Reilly UM (2010) Knowledge mining with genetic programming methods for variable selection in flavor design. In: Branke J, Pelikan M, Alba E, Arnold DV, Bongard J, Brabazon A, Branke J, Butz MV, Clune J, Cohen M, Deb K, Engelbrecht AP, Krasnogor N, Miller JF, O’Neill M, Sastry K, Thierens D, van Hemert J, Vanneschi L, Witt C (eds) GECCO '10: Proceedings of the 12th annual conference on genetic and evolutionary computation, ACM, Portland, Oregon, USA, pp 941–948. doi:10.1145/1830483.1830651
Wagner N, Michalewicz Z, Khouja M, McGregor RR (2007) Time series forecasting for dynamic environments: the DyFor genetic program model. IEEE Trans Evolut Comput 11(4):433–452. doi:10.1109/TEVC.2006.882430
Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(2):69–101
Winkler S, Efendic H, Del Re L, Affenzeller M, Wagner S (2007a) Online modelling based on genetic programming. Int J Intell Syst Technol Appl 2(2/3):255–270
Winkler SM, Affenzeller M, Wagner S (2007b) Selection pressure driven sliding window genetic programming. Lecture Notes in Computer Science 4739: Computer Aided Systems Theory - EuroCAST 2007, pp 789–795
Zuo J, Tang Cj, Li C, Yuan Ca, Chen Al (2004) Time series prediction based on gene expression programming. In: Li Q, Wang G, Feng L (eds) Advances in Web-Age Information Management, Lecture Notes in Computer Science, vol 3129. Springer, Berlin, pp 55–64
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Winkler, S., Affenzeller, M., Kronberger, G., Kommenda, M., Burlacu, B., Wagner, S. (2015). Sliding Window Symbolic Regression for Detecting Changes of System Dynamics. In: Riolo, R., Worzel, W., Kotanchek, M. (eds) Genetic Programming Theory and Practice XII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-16030-6_6
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DOI: https://doi.org/10.1007/978-3-319-16030-6_6
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