Multi-objective symbolic regression for physics-aware dynamic modeling
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- @Article{KUBALIK:2021:ESA,
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author = "Jiri Kubalik and Erik Derner and Robert Babuska",
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title = "Multi-objective symbolic regression for physics-aware
dynamic modeling",
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journal = "Expert Systems with Applications",
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volume = "182",
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pages = "115210",
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year = "2021",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2021.115210",
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URL = "https://www.sciencedirect.com/science/article/pii/S0957417421006436",
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Model learning for control, Motion
control",
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abstract = "Virtually all dynamic system control methods benefit
from the availability of an accurate mathematical model
of the system. This includes also methods like
reinforcement learning, which can be vastly sped up and
made safer by using a dynamic system model. However,
obtaining a sufficient amount of informative data for
constructing dynamic models can be difficult.
Consequently, standard data-driven model learning
techniques using small data sets that do not cover all
important properties of the system yield models that
are partly incorrect, for instance, in terms of their
steady-state characteristics or local behavior.
However, often some knowledge about the desired
physical properties of the model is available.
Recently, several symbolic regression approaches making
use of such knowledge to compensate for data
insufficiency were proposed. Therefore, this knowledge
should be incorporated into the model learning process
to compensate for data insufficiency. In this paper, we
consider a multi-objective symbolic regression method
that optimizes models with respect to their training
error and the measure of how well they comply with the
desired physical properties. We propose an extension to
the existing algorithm that helps generate a diverse
set of high-quality models. Further, we propose a
method for selecting a single final model out of the
pool of candidate output models. We experimentally
demonstrate the approach on three real systems: the
TurtleBot 2 mobile robot, the Parrot Bebop 2 drone and
the magnetic manipulation system. The results show that
the proposed model-learning algorithm yields accurate
models that are physically justified. The improvement
in terms of the model's compliance with prior knowledge
over the models obtained when no prior knowledge was
involved in the learning process is of several orders
of magnitude",
- }
Genetic Programming entries for
Jiri Kubalik
Erik Derner
Robert Babuska
Citations