Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals

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@InProceedings{6339,
  author =       "Michael Affenzeller and Bogdan Burlacu and 
                 Stephan M. Winkler and Michael Kommenda and 
                 Gabriel K. Kronberger and Stefan Wagner",
  title =        "Offspring Selection Genetic Algorithm Revisited:
                 Improvements in Efficiency by Early Stopping Criteria
                 in the Evaluation of Unsuccessful Individuals",
  booktitle =    "16th International Conference on Computer Aided
                 Systems Theory, EUROCAST 2017",
  year =         "2017",
  editor =       "Roberto Moreno-Diaz and Franz Pichler and 
                 Alexis Quesada-Arencibia",
  volume =       "10671",
  series =       "Lecture Notes in Computer Science",
  pages =        "424--431",
  address =      "Las Palmas de Gran Canaria, Spain",
  month =        feb,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-74718-7",
  DOI =          "doi:10.1007/978-3-319-74718-7_51",
  URL =          "https://link.springer.com/chapter/10.1007/978-3-319-74718-7_51",
  abstract =     "This paper proposes some algorithmic extensions to the
                 general concept of offspring selection which itself is
                 an algorithmic extension of genetic algorithms and
                 genetic programming. Offspring selection is
                 characterized by the fact that many offspring solution
                 candidates will not participate in the ongoing
                 evolutionary process if they do not achieve the success
                 criterion. The algorithmic enhancements proposed in
                 this contribution aim to early estimate if a solution
                 candidate will not be accepted based on partial
                 solution evaluation. The qualitative characteristics of
                 offspring selection are not affected by this means. The
                 discussed variant of offspring selection is analysed
                 for several symbolic regression problems with offspring
                 selection genetic programming. The achievable gains in
                 terms of efficiency are remarkable especially for large
                 data-sets.",
}

Genetic Programming entries for Michael Affenzeller Bogdan Burlacu Stephan M Winkler Michael Kommenda Gabriel Kronberger Stefan Wagner

Citations