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A Binary Encoding Supporting Both Mutation and Recombination

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Parallel Problem Solving from Nature, PPSN XI (PPSN 2010)

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Abstract

There has been a long debate on the “most important” operator when applying genetic algorithms. This is very closely related to the favorite binary encoding, namely standard binary or Gray. Rather than confronting both approaches, this article is motivated by the search for an encoding that supports both mutation and recombination. For this purpose an encoding scheme is proposed and evaluated both using metrics and experiments.

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References

  1. Caruana, R.A., Schaffer, J.D.: Representation and hidden bias: Gray versus binary coding in genetic algorithms. In: Leard, J. (ed.) Proc. of the 5th Int. Conf. on Machine Learning, pp. 153–161. Morgan Kaufmann, San Mateo (1988)

    Google Scholar 

  2. Caruana, R.A., Schaffer, J.D., Eshelman, L.J.: Using multiple representations to improve inductive bias: Gray and binary coding for genetic algorithms. In: Segre, A.M. (ed.) Proc. of the Sixth Int. Workshop on Machine Learning, pp. 375–378. Morgan Kaufmann, San Mateo (1989)

    Google Scholar 

  3. Chakraborty, U.K., Janikow, C.Z.: An analysis of gray versus binary encoding in genetic search. Information Sciences 156, 253–269 (2003)

    Article  MathSciNet  Google Scholar 

  4. Collins, J.J., Eaton, M.: Genocodes for genetic algorithms. In: Proc. of Mendel 1997, pp. 23–30. PC-DIR, Brno (1997)

    Google Scholar 

  5. Das, R., Whitley, D.L.: The only challenging problems are deceptive: Global search by solving order-1 hyperplanes. In: Belew, R.K., Booker, L.B. (eds.) Proc. of the Fourth Int. Conf. on Genetic Algorithms, pp. 166–173. Morgan Kaufmann, San Mateo (1991)

    Google Scholar 

  6. Deb, K., Goldberg, D.E.: Sufficient conditions for arbitrary binary functions. Ann. Math. Artificial Intell. 10, 385–408 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  7. Eaton, M., Collins, J.J.: A comparison of encoding schemes for genetic algorithms. In: Proc. of the World Congress on Neural Networks, pp. 1067–1070. INNS Press, New York (1996)

    Google Scholar 

  8. Forrest, S., Mitchell, M.: Relative building-block fitness and the building-block hypothesis. In: Whitley, L.D. (ed.) Foundations of Genetic Algorithms 2, pp. 109–126. Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

  9. Goldberg, D.E.: Zen and the art of genetic algorithms. In: Schaffer, J.D. (ed.) Proc. of the Third Int. Conf. on Genetic Algorithms, pp. 80–85. Morgan Kaufmann, San Mateo (1989)

    Google Scholar 

  10. Holland, J.H.: Adaptation in Natural and Artifical Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  11. Mathias, K., Whitley, D.: Transforming the search space with Gray coding. In: IEEE Conf. onf Evolutionary Computation, pp. 513–518. IEEE Service Center, Piscataway (1994)

    Google Scholar 

  12. Mühlenbein, H.: How genetic algorithms really work: I. Mutation and hillclimbing. In: Männer, R., Manderick, B. (eds.) Parallel Problem Solving from Nature 2, pp. 15–25. Elsevier Science, Amsterdam (1992)

    Google Scholar 

  13. Rana, S.B., Whitley, L.D.: Bit representations with a twist. In: Bäck, T. (ed.) Proc. of the Seventh Int. Conf. on Genetic Algorithms, pp. 188–195. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  14. Rothlauf, F.: Binary representations of integers and the performance of selectorecombinative genetic algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 99–108. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  15. Rothlauf, F.: Representations for Genetic and Evolutionary Algorithms, 2nd edn. Springer, Heidelberg (2006)

    Google Scholar 

  16. Weicker, K.: Auf der Suche nach dem Codierunsgral für genetische Algorithmen. In: Diekert, V., Weicker, K., Weicker, N. (eds.) Informatik als Dialog zwischen Theorie und Anwendung, pp. 35–44. Vieweg+Teubner, Wiesbaden (2009)

    Chapter  Google Scholar 

  17. Whitley, D., Rana, S.: Representation, search and genetic algorithms. In: AAAI 1997: 14th National Conf. on Artificial Intelligence, pp. 497–502. AAAI Press/MIT Press (1997)

    Google Scholar 

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Weicker, K. (2010). A Binary Encoding Supporting Both Mutation and Recombination. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_14

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  • DOI: https://doi.org/10.1007/978-3-642-15844-5_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15843-8

  • Online ISBN: 978-3-642-15844-5

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