The Relationship between Semantic Distance and Performance in Dynamic Symbolic Regression Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{tuite:mendel:2014,
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author = "Cliodhna Tuite and Michael O'Neill and
Anthony Brabazon",
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title = "The Relationship between Semantic Distance and
Performance in Dynamic Symbolic Regression Problems",
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booktitle = "Mendel 2014 The 20th International Conference on Soft
Computing",
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year = "2014",
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address = "Brno, Czech Republic",
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keywords = "genetic algorithms, genetic programming, dynamic
environments, symbolic regression, semantic distance",
-
URL = "http://ncra.ucd.ie/papers/mendel2014_tuite.pdf",
-
abstract = "Several methods which apply genetic programming (GP)
in dynamic optimisation environments implicitly assume
that the smaller the semantic change in the
optimization goal, the better the adaptation of the GP
population to the new target. However, GP searches over
genetic operator-based fitness landscapes. As such,
relative distances between solution points in genetic
operator-based landscapes may not be related to the
semantic distances between points. Our experiments
examine whether decreasing the semantic distance
between first and second-period target functions in
symbolic regression problems result in improved
performance in the second period. As a control, we also
investigate how re-initialising the GP population in
the second period performs in comparison with using a
continuous GP population across the two periods. We
find that decreasing the semantic distance does result
in better performance in the second period, and that
re-initializing the GP population under performs a
continuous population at low semantic distances.",
-
notes = "http://www.mendel-conference.org/tmp/ScheduleMendel2014e.pdf",
- }
Genetic Programming entries for
Cliodhna Tuite
Michael O'Neill
Anthony Brabazon
Citations