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Adaptive strategies for a semantically driven tree optimizer to control code growth

Published: 07 July 2007 Publication History

Abstract

In genetic programming many methods to fight growth exist. Butmost of these methods require one or multiple parameters to beset. Unfortunately performance strongly depends on a correct setting of each of those parameters. Recently a semantically driven tree optimizer has been developed. In this paper two adaptive strategies to choose a reasonable parameter setting forthis growth limiter are presented.

Reference

[1]
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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  1. Adaptive strategies for a semantically driven tree optimizer to control code growth

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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. Boolean problems
      2. adaptive control
      3. artificial ant
      4. code growth
      5. fuzzy logic
      6. genetic programming

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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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