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On Fitness Distributions and Expected Fitness Gain of Mutation Rates in Parallel Evolutionary Algorithms

  • David W. Corne
  • Martin J. Oates
  • Douglas B. Kell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

Setting the mutation rate for an evolutionary algorithm (EA) is confounded by many issues. Here we investigate mutation rates mainly in the context of large-population-parallelism. We justify the notion that high rates achieve better results, using underlying theory which notices that parallelization favourably alters the fitness distribution of a mutation operator. We derive an expression which sets out how this is changed in terms of the level of parallelization, and derive further expressions that allow us to adapt the mutation rate in a principled way by exploiting online-sampled landscape information. The adaptation technique (called RAGE– Rate Adaptation with Gain Expectation) shows promising preliminary results. Our motivation is the field of Directed Evolution (DE), which uses large-scale parallel EAs for limited numbers of generations to evolve novel proteins. RAGE is highly suitable for DE, and is applicable to large-scale parallel EAs in general.

Keywords

Mutation Rate Direct Evolution Fitness Distribution Adaptation Technique Promising Preliminary Result 
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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References

  1. 1.
    Arnold, F. M. (ed). Evolutionary protein design. Advances in Protein Chemistry, vol. 55. Academic Press, San Diego, 2001.Google Scholar
  2. 2.
    Arnold F. Combinatorial and computational challenges for biocatalyst design. Nature 2001;409:253–7.CrossRefGoogle Scholar
  3. 3.
    Bäck T, Optimal Mutation Rates in Genetic Search, Proc. 5th ICGA, pp 2–9, 1993.Google Scholar
  4. 4.
    Bäck T, Evolutionary Algorithms in Theory and Practice, OUP, 1996.Google Scholar
  5. 5.
    Baltz, RH. Mutation in Streptomyces. In: Day L, Queener S, editors. The Bacteria, Vol 9, Antibiotic-producing Streptomyces. Academic Press, 1986:61–94.Google Scholar
  6. 6.
    Blickle, T., Thiele, L. (1995). A Mathematical Analysis of Tournament Selection, in L.J. Eshelman (ed.) Proc. 6th International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 9–16.Google Scholar
  7. 7.
    Cantú-Paz, E. (2000). Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers.Google Scholar
  8. 8.
    Fogel, D.B. and Ghozeil, A. (1996). Using Fitness Distributions to Design More Efficient Evolutionary Computations, in Proceedings of the 3rd International Conference on Evolutionary Computation, IEEE, pp. 11–19.Google Scholar
  9. 9.
    Mühlenbein, H. How genetic algorithms really work: I. Mutation and Hillclimbing, in R. Manner, B. Manderick (eds), Proc. 2nd Int’l Conf. on Parallel Problem Solving from Nature, Elsevier, pp 15–25.Google Scholar
  10. 10.
    Oates, M. and Corne, D. Overcoming Fitness Barriers in Multi-Modal Search Spaces, in Foundations of Genetic Algorithms 6 (2000), Morgan Kaufmann.Google Scholar
  11. 11.
    Rechenberg I, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Frommann-Holzboog, Stuttgart, 1973Google Scholar
  12. 12.
    Voigt CA, Kauffman S & Wang ZG. Rational evolutionary design: The theory of in vitro protein evolution. In: Arnold FM, editor. Advances in Protein Chemistry, Vol 55, 2001:79–160.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • David W. Corne
    • 1
  • Martin J. Oates
    • 2
  • Douglas B. Kell
    • 3
  1. 1.Department of Computer ScienceUniversity of ReadingUK
  2. 2.Evosolve LtdStowmarket, SuffolkUK
  3. 3.Institute of Biological SciencesUniversity of WalesAberystwythUK

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