Complexity Measures for Multi-Objective Symbolic Regression
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- @InProceedings{4593,
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author = "Michael Kommenda and Andreas Beham and
Michael Affenzeller and Gabriel K. Kronberger",
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title = "Complexity Measures for Multi-Objective Symbolic
Regression",
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booktitle = "Computer Aided Systems Theory, EUROCAST 2015",
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year = "2015",
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editor = "Roberto Moreno-Diaz and Franz Pichler and
Alexis Quesada-Arencibia",
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volume = "9520",
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series = "Lecture Notes in Computer Science",
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pages = "409--416",
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address = "Las Palmas, Gran Canaria, Spain",
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month = feb,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Complexity measures, Multi-objective
optimization, NSGA-II",
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isbn13 = "978-3-319-27340-2",
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URL = "https://link.springer.com/chapter/10.1007/978-3-319-27340-2_51",
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DOI = "doi:10.1007/978-3-319-27340-2_51",
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abstract = "Multi-objective symbolic regression has the advantage
that while the accuracy of the learned models is
maximized, the complexity is automatically adapted and
need not be specified a-priori. The result of the
optimization is not a single solution any more, but a
whole Pareto-front describing the trade-off between
accuracy and complexity.
In this contribution we study which complexity measures
are most appropriately used in symbolic regression when
performing multi- objective optimization with NSGA-II.
Furthermore, we present a novel complexity measure that
includes semantic information based on the function
symbols occurring in the models and test its effects on
several benchmark datasets. Results comparing multiple
complexity measures are presented in terms of the
achieved accuracy and model length to illustrate how
the search direction of the algorithm is affected.",
- }
Genetic Programming entries for
Michael Kommenda
Andreas Beham
Michael Affenzeller
Gabriel Kronberger
Citations