Structure-Based Constants in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Veenhuis:2013:EPIA,
  author =       "Christian B. Veenhuis",
  title =        "Structure-Based Constants in Genetic Programming",
  booktitle =    "Proceedings of the 16th Portuguese Conference on
                 Artificial Intelligence, EPIA 2013",
  year =         "2013",
  editor =       "Luis Correia and Luis Paulo Reis and Jose Cascalho",
  volume =       "8154",
  series =       "Lecture Notes in Computer Science",
  pages =        "126--137",
  address =      "Angra do Heroismo, Azores, Portugal",
  month =        sep # " 9-12",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Constant,
                 Structure-based Constant, Constant Function, Subtree
                 Relationship, Full Tree Normalisation, Generic
                 Benchmark, Polynomial Benchmark, Sum-of-Gaussians
                 Benchmark",
  isbn13 =       "978-3-642-40668-3",
  URL =          "http://link.springer.com/chapter/10.1007%2F978-3-642-40669-0_12",
  DOI =          "doi:10.1007/978-3-642-40669-0_12",
  size =         "12",
  abstract =     "Evolving constants in Genetic Programming is still an
                 open issue. As real values they cannot be integrated in
                 GP trees in a direct manner, because the nodes
                 represent discrete symbols. Present solutions are the
                 concept of ephemeral random constants or hybrid
                 approaches, which have additional computational costs.
                 Furthermore, one has to change the GP algorithm for
                 them. This paper proposes a concept, which does not
                 change the GP algorithm or its components. Instead, it
                 introduces structure-based constants realised as
                 functions, which can be simply added to each function
                 set while keeping the original GP approach. These
                 constant functions derive their constant values from
                 the tree structures of their child-trees (subtrees).
                 That is, a constant is represented by a tree structure
                 being this way under the influence of the typical
                 genetic operators like subtree crossover or mutation.
                 These structure-based constants were applied to
                 symbolic regression problems. They outperformed the
                 standard approach of ephemeral random constants. Their
                 results together with their better properties make the
                 structure-based constant concept a possible candidate
                 for the replacement of the ephemeral random
                 constants.",
}

Genetic Programming entries for Christian Veenhuis

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