Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques   [GP]

by

Piszcz, A. and Soule, T.

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Info: Genetic and Evolutionary Computation Conference (GECCO2005) workshop program (Conference proceedings), 2005, p. 220-227
Keywords:genetic algorithms, genetic programming
Abstract:
Abstract. We suggest that the relationship between parameter settings, ie parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programming environments [GP] [GP] [PE] have few means for a priori determination of appropriate parameters values.The hypothesised nonlinear behaviour of genetic programming [GP] creates difficulty in selecting parameter values for many problems. we study three structure altering mutation techniques using parametric analysis on a problem with scalable complexity. We nd through parameter analysis that two of the three mutation types tested exhibit nonlinear behaviour. Higher mutation rates cause a larger degree of nonlinear behaviour as measured by fitness and computational effort. [CE] Characterisation of the mutation techniques using parametric analysis confirms the nonlinear behavior. In addition, we propose an extension to the existing parameter setting taxonomy to [PS] include commonly used structure altering mutation attributes. Finally we show that the proportion of mutations applied to internal nodes, instead of leaf nodes, has a significant effect on performance.
Notes:
Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006
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BibTex:
@InProceedings(piszcz:gecco05ws,
  author = {Alan Piszcz and Terence Soule},
  title = {Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques},
  booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf and Misty Blowers and  J{\"u}rgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J{\"o}rn Grahl and Gregory Hornby and Edwin D. {de Jong} and Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor{\`a} and  Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton and Alden H. Wright},
  publisher = {ACM Press},
  address = {Washington, D.C., USA},
  keywords =  {genetic algorithms, genetic programming},
  pages = {220--227},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0220.pdf},
  abstract = {Abstract. We suggest that the relationship between parameter settings, ie parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programming environments have few means for a priori determination of appropriate parameters values.The hypothesised nonlinear behaviour of genetic programming creates difficulty in selecting parameter values for many problems. we study three structure altering mutation techniques using parametric analysis on a problem with scalable complexity. We nd through parameter analysis that two of the three mutation types tested exhibit nonlinear behaviour. Higher mutation rates cause a larger degree of nonlinear behaviour as measured by fitness and computational effort. Characterisation of the mutation techniques using parametric analysis confirms the nonlinear behavior. In addition, we propose an extension to the existing parameter setting taxonomy to include commonly used structure altering mutation attributes. Finally we show that the proportion of mutations applied to internal nodes, instead of leaf nodes, has a significant effect on performance. },
  notes = {Distributed on CD-ROM at GECCO-2005.
ACM 1-59593-097-3/05/0006},
)