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Universal information distance for genetic programming

Published: 12 July 2014 Publication History

Abstract

This paper presents a genotype-level distance metric for Genetic Programming (GP) based on the symmetric difference concept: first, the information contained in individuals is expressed as a set of symbols (the content of each node, its position inside the tree, and recurring parent-child structures); then, the difference between two individuals is computed considering the number of elements belonging to one, but not both, of their symbol sets.

References

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Symmetric Difference. In E. J. Borowski and J. M. Borwein, editors, The HarperCollins Dictionary of Mathematics. HarperCollins, 1991.
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M. Gaudesi, G. Squillero, and A. Tonda. An Efficient Distance Metric for Linear Genetic Programming. pages 925--932. GECCO '13, ACM, 2013.
[3]
R. I. McKay. Fitness Sharing in Genetic Programming. In GECCO, pages 435--442, 2000.
[4]
R. Poli, W. W. B. Langdon, N. F. McPhee, and J. R. Koza. A field guide to genetic programming. Lulu.com, 2008.
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C. D. Rosin and R. K. Belew. New methods for competitive coevolution. Evolutionary Computation, 5(1):1--29, 1997.
[6]
B. Sareni and L. Krahenbuhl. Fitness sharing and niching methods revisited. Evolutionary Computation, IEEE Transactions on, 2(3):97--106, 1998.
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M. Schmidt and H. Lipson. Distilling free-form natural laws from experimental data. Science, 324(5923):81--85, 2009.
[8]
C. Suen. N-gram statistics for natural language understanding and text processing. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (2):164--172, 1979.

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cover image ACM Conferences
GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
July 2014
1524 pages
ISBN:9781450328814
DOI:10.1145/2598394
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

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Author Tags

  1. algorithms
  2. distance metric
  3. diversity preservation
  4. experimental analysis
  5. fitness sharing
  6. genetic programming
  7. individual encoding
  8. measurements
  9. symbolic regression

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GECCO '14
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GECCO '14: Genetic and Evolutionary Computation Conference
July 12 - 16, 2014
BC, Vancouver, Canada

Acceptance Rates

GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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