Sliding Window Symbolic Regression for Detecting Changes of System Dynamics
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- @InProceedings{Winkler:2014:GPTP,
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author = "Stephan M. Winkler and Michael Affenzeller and
Gabriel Kronberger and Michael Kommenda and Bogdan Burlacu and
Stefan Wagner",
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title = "Sliding Window Symbolic Regression for Detecting
Changes of System Dynamics",
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booktitle = "Genetic Programming Theory and Practice XII",
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year = "2014",
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editor = "Rick Riolo and William P. Worzel and Mark Kotanchek",
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series = "Genetic and Evolutionary Computation",
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pages = "91--107",
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address = "Ann Arbor, USA",
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month = "8-10 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Self-adaptive sliding window techniques,
System analysis, System dynamics",
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isbn13 = "978-3-319-16029-0",
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DOI = "doi:10.1007/978-3-319-16030-6_6",
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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 analysed 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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notes = "
Part of \cite{Riolo:2014:GPTP} published after the
workshop in 2015",
- }
Genetic Programming entries for
Stephan M Winkler
Michael Affenzeller
Gabriel Kronberger
Michael Kommenda
Bogdan Burlacu
Stefan Wagner
Citations