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The Genetic Code-Like Transformations and Their Effect on Learning Functions

  • Hillol Kargupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1917)

Abstract

The natural gene expression process evaluates the fitness of a DNA-string through a sequence of representation transformations. The genetic code defines one such transformation in this process. This paper shows that genetic code-like transformations introduce an interesting property in the representation of a genetic fitness function. It points out that such adaptive transformations can convert some functions with an exponentially large description in Fourier basis to one that is highly suitable for polynomial-size approximation. Such transformations can construct a Fourier representation with only a polynomial number of terms that axe exponentially more significant than the rest when fitter chromosomes are given more copies through a redundant, equivalent representation. This is a very desirable property [2, 3] for efficient function-induction from data which is a fundamental problem in learning, data mining, and optimization.

Keywords

Genetic Code Fourier Coefficient Protein Feature Fourier Representation Fourier Basis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Hillol Kargupta
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceWashington State UniversityPullmanUSA

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