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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Beauchamp, K.G.: Applications of Walsh and Related Functions. Academic Press, USA (1984)Google Scholar
  2. 2.
    Kushilevitz, S., Mansour, Y.: Learning decision rees using Fourier spectrum. In: Proc. 23rd Annual ACM Symp. on Theory of Computing. (1991) 455–464Google Scholar
  3. 3.
    Jackson, J.: The Harmonic Sieve: A Novel Application of Fourier Analysis to Machine Learning Theory and Practice. PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA (1995)Google Scholar
  4. 4.
    Jacob, F., Monod, J.: Genetic regulatory mechanisms in the synthesis of proteins. Molecular Biology 3 (1961) 318–356CrossRefGoogle Scholar
  5. 5.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  6. 6.
    Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3 (1989) 493–530 (Also TCGA Report 89003).zbMATHMathSciNetGoogle Scholar
  7. 7.
    Kauffman, S.: The Origins of Order. Oxford University Press, New York (1993)Google Scholar
  8. 8.
    Keller, R., Banzhaf, W.: The evolution of genetic code in genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann Publishers (1999) 1077–1082Google Scholar
  9. 9.
    Reidys, C, Fraser, S.: Evolution of random structures. Technical Report 96-11-082, Santa Fe Institute, Santa Fe (1996)Google Scholar
  10. 10.
    Hornos, J., Hornos, Y.: Algebraic model for the evolution of the genetic code. Physical Review Letters 71 (1993) 4401–4404CrossRefGoogle Scholar
  11. 11.
    Beland, P., Allen, T.: The origin and evolution of the genetic code. Journal of Theoretical Biology 170 (1994) 359–365CrossRefGoogle Scholar
  12. 12.
    Kargupta, H.: Gene Expression: The missing link of evolutionary computation. In D. Quagliarella, J. Periaux, C.P., Winter, G., eds.: Genetic Algorithms in Engineering and Computer Science. John Wiley & Sons Ltd. (1997) Chapter 4Google Scholar
  13. 13.
    Kargupta, H., Park, H.: Fast construction of distributed and decomposed evolutionary representation. Accepted for publication in the Journal of Evolutionary Computation, MIT Press. (2000)Google Scholar
  14. 14.
    Kargupta, H.: A striking property of genetic code-like transformations. School of EECS Technical Report EECS-99-004, Washington State University, Pullman, WA (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

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

Personalised recommendations