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