Skip to main content
SpringerLink
Log in

An individually variable mutation-rate strategy for genetic algorithms

  • Enhanced Evolutionary Operators
  • Conference paper
  • First Online:
Evolutionary Programming VI (EP 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1213))

Included in the following conference series:

Abstract

In Neo-Darwinism, mutation can be considered to be unaffected by selection pressure. This is the metaphor generally used by the genetic algorithm for its treatment of the mutation operation, which is usually regarded as a background operator. This metaphor, however, does not take into account the fact that mutation has been shown to be affected by external events. In this paper, we propose a mutation-rate strategy that is variable between individuals within a given generation based on the individual's relative performance for the purpose of function optimization.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. Bäck, T. 1991. A Survey of Evolution Strategies. Proceedings of the Fourth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann. Pages 2–9.

    Google Scholar 

  2. Bäck, T. 1992a. The Interaction of Mutation Rate, Selection, and Self-Adaptation within a Genetic Algorithm. In Männer, R. and Manderick, B. (editors). Problem Solving from Nature 2. Amsterdam: North Holland. Pages 85–94.

    Google Scholar 

  3. Bäck, T. 1992b. Self-Adaptation in Genetic Algorithms. In Varela, F.J. and Borgine, R. (editors). Proceedings of the First European Conference on Artificial Life. Cambridge, MA: The MIT Press. Pages 263–271.

    Google Scholar 

  4. Bäck, T. 1993. Optimal Mutation Rates in Genetic Search. In Forrest, S. (editor). Proceedings of the Fifth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann. Pages 2–8.

    Google Scholar 

  5. Bäck, T. and Schwefel, H.P. 1995. In Winter, G., and Periaux, J. (editors). Genetic Algorithms in Engineering and Computer Science. New York: John Wiley & Sons Ltd. Pages 127–140.

    Google Scholar 

  6. Bernhard, W. and Illi, H. 1994. In Halen, Karplus, and Rimane (editors). CISS — First Joint Conference on International Simulation Societies. Pages 111–115.

    Google Scholar 

  7. Boesen, J.B. et al. 1992. Stress response induced by DNA damage leads to specific, delayed and untargeted mutations. Molecular and General Genetics. Volume 234. Pages 217–227.

    PubMed  Google Scholar 

  8. Bramlette, M.F. In Belew, R.K. and Booker L.B. (editors). Proceedings of the Fourth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann. Pages 100–107.

    Google Scholar 

  9. Calow, P. Evolutionary Principles. Glasgow: Blackie & Son Ltd.

    Google Scholar 

  10. Cobb, H.G. 1990. An Investigation into the Use of Hypermutation as an Adaptive Operator in Genetic Algorithms Having Continuous, Time-Dependent Nonstationary Environments. NRL Memorandum Report 6760.

    Google Scholar 

  11. Cobb, H.G. and Grefenstette, J.J. 1993. Genetic Algorithms for Tracking Changing Environments. In Forrest, S. (editor). Proceedings of the Fifth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann. Pages 523–529.

    Google Scholar 

  12. Dawkins, R. 1987. The Blind Watchmaker. New York, NY: W.W. Norton & Company, Inc.

    Google Scholar 

  13. DeJong, K.A. 1975. Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. Dissertation. University of Michigan, Ann Arbor.

    Google Scholar 

  14. Fogarty, T.C. 1989. Varying the Probability of Mutation in the Genetic Algorithm. In Schaffer, J.D. (editor). Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann. Pages 104–109.

    Google Scholar 

  15. Grefenstette, J.J. 1986. Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions of Systems, Man, and Cybernetics. Vol. SMC-16, No. 1. Pages 122–128.

    Google Scholar 

  16. Grefenstette, J.J. 1992. Genetic Algorithms for changing environments. In Männer, R. and Manderick, B. (editors). Parallel Problem Solving from Nature 2. Amsterdam: North Holland. Pages 137–144.

    Google Scholar 

  17. Hatada, Y., et al. 1994. Induction of Pleiotropic Mutation in Streptomyces griseus by Incubation under Stress Conditions for Mycelial Growth. Bioscience, Biotechnology, and Biochemistry. Volume 58. Pages 990–991.

    Google Scholar 

  18. Hesser, J. and Männer, R. 1991. Towards an Optimal Mutation Probability for Genetic Algorithms. In Schwefel, H.D. and Männer, R. (editors). Parallel Problem Solving from Nature. New York, NY: Springer LCNS 496.

    Google Scholar 

  19. Holland, J.H. 1975. Adaptation in Natural and Artificial Systems. Cambridge, MA: The MIT Press.

    Google Scholar 

  20. Holland, J.H. 1995. Hidden Order: How Adaptation Builds Complexity. Reading, MA: Addison-Wesley.

    Google Scholar 

  21. Huxley, J. Evolution: The Modern Synthesis. London: Allen and Unwin.

    Google Scholar 

  22. Imlay, J.A. and Linn, S. 1987. Mutagenesis and Stress Responses Induced in Escheria coli by Hydrogen Peroxide. Journal of Bacteriology. Volume 169. Pages 2967–2976.

    PubMed  Google Scholar 

  23. Koza, J.R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: The MIT Press.

    Google Scholar 

  24. Lithgow, G.J. et al. 1995. Thermotolerance and extended life-span conferred by single-gene mutations and induced by thermal stress. Proceedings of the National Academy of Sciences of the United States of America. Volume 92. Pages 7540–7544.

    PubMed  Google Scholar 

  25. MacPhee, D. 1993. Directed Mutation Reconsidered. American Scientist. Volume 81. Pages 554–561.

    Google Scholar 

  26. Mayr, E. The Evolutionary Synthesis: Perspectives on the Unification of Biology. Cambridge, MA: Harvard University Press.

    Google Scholar 

  27. Mühlenbein, H. 1992. How Genetic Algorithms really work: I. mutation and hillclimbing. In Männer, R. and Manderick, B. (editors). Parallel Problem Solving from Nature 2. Amsterdam: North Holland. Pages 15–25.

    Google Scholar 

  28. Mühlenbein, H. and Schlierkamp-Voosen, D. 1995. Analysis of Selection, Mutation and Recombination in Genetic Algorithms. In Banzhaf, W., and Eeckman, F. (editors). Evolution as a Computational Process. Lecture Notes in Computer Science. Berlin: Springer. Pages 188–214.

    Google Scholar 

  29. Rechenberg, I. 1973. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Stuttgart: Frommann-Holzboog.

    Google Scholar 

  30. Sanders, J. et al. 1995. Stress Response in Lactococcus lactis: Cloning, Expression Analysis, and Mutation of the Lactococcal Superoxide Dismutase Gene. Journal of Bacteriology. Volume 177. Pages 5254–5259.

    PubMed  Google Scholar 

  31. Schaffer, J.D. et al. 1989. A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. In Schaffer, J.D. (editor). Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann. Pages 51–60.

    Google Scholar 

  32. Schwefel, H.P. 1977. Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. In Interdisciplinary Systems Research. Basel: Birkhauser.

    Google Scholar 

  33. Schwefel, H.P. 1992. Imitating evolution: Collective, two-level learning processes. In Witt, U. (editor). Explaining Process and Change — Approaches to Evolutionary Economics. Ann Arbor: The University of Michigan Press. Pages 49–63.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Environmental Research Institute of Michigan, Advanced Information Systems Group, P.O. Box 134001, 48113-4001, Ann Arbor, Michigan, USA

    Stephen A. Stanhope

  2. Artificial Intelligence Laboratory & Space Physics Research Laboratory, The University of Michigan, 2455 Hayward Avenue, 48109-2143, Ann Arbor, Michigan, USA

    Jason M. Daida

Authors
  1. Stephen A. Stanhope
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jason M. Daida
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Peter J. Angeline Robert G. Reynolds John R. McDonnell Russ Eberhart

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Stanhope, S.A., Daida, J.M. (1997). An individually variable mutation-rate strategy for genetic algorithms. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhart, R. (eds) Evolutionary Programming VI. EP 1997. Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014815

Download citation

  • DOI: https://doi.org/10.1007/BFb0014815

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62788-3

  • Online ISBN: 978-3-540-68518-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation