Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals
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- @InProceedings{6339,
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author = "Michael Affenzeller and Bogdan Burlacu and
Stephan M. Winkler and Michael Kommenda and
Gabriel K. Kronberger and Stefan Wagner",
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title = "Offspring Selection Genetic Algorithm Revisited:
Improvements in Efficiency by Early Stopping Criteria
in the Evaluation of Unsuccessful Individuals",
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booktitle = "16th International Conference on Computer Aided
Systems Theory, EUROCAST 2017",
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year = "2017",
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editor = "Roberto Moreno-Diaz and Franz Pichler and
Alexis Quesada-Arencibia",
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volume = "10671",
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series = "Lecture Notes in Computer Science",
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pages = "424--431",
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address = "Las Palmas de Gran Canaria, Spain",
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month = feb,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-74718-7",
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DOI = "doi:10.1007/978-3-319-74718-7_51",
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URL = "https://link.springer.com/chapter/10.1007/978-3-319-74718-7_51",
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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