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Limiting code growth to improve robustness in tree-based genetic programming

Published: 07 July 2007 Publication History

Abstract

In this paper we analyze the composition of the function set ofthe artificial ant problem to define new training and testing trails similar to the Santa Fe trail. Cross-validation is used inapplications where large amounts of data are available. We alsouse a semantically driven growth limiter to reduce program sizeand check if growth reduction could lead to increased testperformance.

References

[1]
S. Luke. Modification point depth and genome growth in genetic programming. Evolutionary Computation, 11(1):67--106, 2003.
[2]
B. Wyns, S. Sette, and L. Boullart. Self-improvement to control code growth in genetic programming. Lecture Notes in Computer Science, 2936: 256--266, 2004.

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  • (2008)A survey and taxonomy of performance improvement of canonical genetic programmingKnowledge and Information Systems10.1007/s10115-008-0184-921:1(1-39)Online publication date: 12-Dec-2008

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  1. Limiting code growth to improve robustness in tree-based genetic programming

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      cover image ACM Conferences
      GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
      July 2007
      2313 pages
      ISBN:9781595936974
      DOI:10.1145/1276958

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      Association for Computing Machinery

      New York, NY, United States

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      Published: 07 July 2007

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      Author Tags

      1. artificial ant
      2. code growth
      3. genetic programming
      4. representational bias
      5. robustness

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      GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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      • (2008)A survey and taxonomy of performance improvement of canonical genetic programmingKnowledge and Information Systems10.1007/s10115-008-0184-921:1(1-39)Online publication date: 12-Dec-2008

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