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EC Theory: A Unified Viewpoint

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2724))

Abstract

In this paper we show how recent theoretical developments have led to a more unified framework for understanding different branches of Evolutionary Computation (EC), as well as distinct theoretical formulations, such as the Schema theorem and the Vose model. In particular, we show how transformations of the configuration space of the model — such as coarse-grainings/projections, embeddings and coordinate transformations — link these different, disparate elements and alter the standard taxonomic classification of EC and different EC theories. As an illustration we emphasize one particular coordinate transformation between the standard string basis and the “Building Block” basis.

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References

  1. Michael D. Vose. The simple genetic algorithm: Foundations and theory. MIT Press, Cambridge, MA, 1999.

    MATH  Google Scholar 

  2. David E. Goldberg. The Design of Innovation. Kluwer Academic Publishers, Boston, 2002.

    MATH  Google Scholar 

  3. J. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, USA, 1975.

    Google Scholar 

  4. A.E. Nix and M.D. Vose. Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence, 5(1):79–88, April 1992.

    Article  MATH  MathSciNet  Google Scholar 

  5. Adam Prügel-Bennett and Jonathan L. Shapiro. An analysis of genetic algorithms using statistical mechanics. Physical Review Letters, 72:1305–1309, 1994.

    Article  Google Scholar 

  6. David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading MA, 1989.

    MATH  Google Scholar 

  7. C.R. Stephens and H. Waelbroeck. Effective degrees of freedom of genetic algorithms and the block hypothesis. In T. Bäck, editor, Proceedings of ICGA97, pages 31–41, San Francisco, CA, 1997. Morgan Kaufmann.

    Google Scholar 

  8. C.R. Stephens and H. Waelbroeck. Schemata evolution and building blocks. Evol. Comp., 7:109–124, 1999.

    Article  Google Scholar 

  9. C.R. Stephens. Some exact results from a coarse grained formulation of genetic dynamics. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 631–638, San Francisco, California, USA, 7–11 July 2001. Morgan Kaufmann.

    Google Scholar 

  10. Riccardo Poli. Exact schema theorem and effective fitness for GP with one-point crossover. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 469–476, Las Vegas, July 2000. Morgan Kaufmann.

    Google Scholar 

  11. Riccardo Poli. Exact schema theory for genetic programming and variable-length genetic algorithms with one-point crossover. Genetic Programming and Evolvable Machines, 2(2):123–163, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  12. W.B. Langdon and R. Poli. Foundations of Genetic Programming. Springer Verlag, Berlin, New York, 2002.

    Book  MATH  Google Scholar 

  13. Peter Nordin. Evolutionary Program Induction of Binary Machine Code and its Applications. PhD thesis, der Universitat Dortmund am Fachereich Informatik, 1997.

    Google Scholar 

  14. Michael O’Neill and Conor Ryan. Grammatical evolution. IEEE Transaction on Evolutionary Compuation, 5(4), August 2001.

    Google Scholar 

  15. A. Aguilar, J. Rowe, and C.R. Stephens. Coarse graining in genetic algorithms: Some issues and examples. In E. Cantú-Paz, editor, Proceedings of GECCO 2003, Chicago, 9–13 July 2003. Springer-Verlag.

    Google Scholar 

  16. Edward D. Weinberger. Fourier and Taylor series on fitness landscapes. Biological Cybernetics, 65:321–330, 1991.

    Article  MATH  Google Scholar 

  17. C.R. Stephens. The renormalization group and the dynamics of genetic systems. Acta Phys. Slov., 52:515–524, 2002.

    Google Scholar 

  18. R. Poli, J.E. Rowe, C.R. Stephens, and A.H. Wright. Allele diffusion in linear genetic programming and variable-length genetic algorithms with subtree crossover. In Proceedings of EuroGP 2002, LNCS, Vol. 2278, pages 211–227. Springer-Verlag, 2002.

    Google Scholar 

  19. R. Poli, C.R. Stephens, J.E. Rowe, and A.H. Wright. On the search biases of homologous crossover in linear genetic programming and variable-length genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), pages 211–227. Morgan Kaufmann, 2002.

    Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Stephens, C.R., Zamora, A. (2003). EC Theory: A Unified Viewpoint. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_13

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  • DOI: https://doi.org/10.1007/3-540-45110-2_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40603-7

  • Online ISBN: 978-3-540-45110-5

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