Cluster Analysis of a Symbolic Regression Search Space
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{kronberger:2018:GPTP,
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author = "Gabriel Kronberger and Lukas Kammerer and
Bogdan Burlacu and Stephan M. Winkler and Michael Kommenda and
Michael Affenzeller",
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title = "Cluster Analysis of a Symbolic Regression Search
Space",
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booktitle = "Genetic Programming Theory and Practice XVI",
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year = "2018",
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editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman",
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pages = "85--102",
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address = "Ann Arbor, USA",
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month = "17-20 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-030-04734-4",
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URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_5",
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DOI = "doi:10.1007/978-3-030-04735-1_5",
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abstract = "In this chapter we take a closer look at the
distribution of symbolic regression models generated by
genetic programming in the search space. The motivation
for this work is to improve the search for well-fitting
symbolic regression models by using information about
the similarity of models that can be precomputed
independently from the target function. For our
analysis, we use a restricted grammar for uni-variate
symbolic regression models and generate all possible
models up to a fixed length limit. We identify unique
models and cluster them based on phenotypic as well as
genotypic similarity. We find that phenotypic
similarity leads to well-defined clusters while
genotypic similarity does not produce a clear
clustering. By mapping solution candidates visited by
GP to the enumerated search space we find that GP
initially explores the whole search space and later
converges to the subspace of highest quality
expressions in a run for a simple benchmark problem.",
- }
Genetic Programming entries for
Gabriel Kronberger
Lukas Kammerer
Bogdan Burlacu
Stephan M Winkler
Michael Kommenda
Michael Affenzeller
Citations