Skip to main content

Pure Strategy or Mixed Strategy?

An Initial Comparison of Their Asymptotic Convergence Rate and Asymptotic Hitting Time

  • Conference paper
Evolutionary Computation in Combinatorial Optimization (EvoCOP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7245))

Abstract

Mixed strategy evolutionary algorithms (EAs) aim at integrating several mutation operators into a single algorithm. However no analysis has been made to answer the theoretical question: whether and when is the performance of mixed strategy EAs better than that of pure strategy EAs? In this paper, asymptotic convergence rate and asymptotic hitting time are proposed to measure the performance of EAs. It is proven that the asymptotic convergence rate and asymptotic hitting time of any mixed strategy (1+1) EA consisting of several mutation operators is not worse than that of the worst pure strategy (1+1) EA using only one mutation operator. Furthermore it is proven that if these mutation operators are mutually complementary, then it is possible to design a mixed strategy (1+1) EA whose performance is better than that of any pure strategy (1+1) EA using only one mutation operator.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Fogel, D., Michalewicz, Z.: Handbook of Evolutionary Computation. Oxford Univ. Press (1997)

    Google Scholar 

  2. Grosan, C., Abraham, A., Ishibuchi, H.: Hybrid Evolutionary Algorithms. Springer (2007)

    Google Scholar 

  3. Dutta, P.: Strategies and Games: Theory and Practice. MIT Press (1999)

    Google Scholar 

  4. He, J., Yao, X.: A Game-Theoretic Approach for Designing Mixed Mutation Strategies. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005, Part III. LNCS, vol. 3612, pp. 279–288. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  5. Dong, H., He, J., Huang, H., Hou, W.: Evolutionary programming using a mixed mutation strategy. Information Sciences 177(1), 312–327 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Shen, L., He, J.: A mixed strategy for evolutionary programming based on local fitness landscape. In: Proceedings of 2010 IEEE Congress on Evolutionary Computation, pp. 350–357. IEEE Press, Barcelona (July 2010)

    Google Scholar 

  7. Varga, R.: Matrix Iterative Analysis. Springer (2009)

    Google Scholar 

  8. He, J., Chen, T.: Population scalability analysis of abstract population-based random search: Spectral radius. Arxiv preprint arXiv:1108.4531 (2011)

    Google Scholar 

  9. Michalewicz, Z.: Genetic Algorithms + Data Structure = Evolution Program. Springer, New York (1996)

    Google Scholar 

  10. He, J., Zhou, Y.: A Comparison of GAs Using Penalizing Infeasible Solutions and Repairing Infeasible Solutions on Average Capacity Knapsack. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 100–109. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  11. Rudolph, G.: Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks 5(1), 96–101 (1994)

    Article  Google Scholar 

  12. He, J., Kang, L.: On the convergence rate of genetic algorithms. Theoretical Computer Science 229(1-2), 23–39 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  13. He, J., Yao, X.: Towards an analytic framework for analysing the computation time of evolutionary algorithms. Artificial Intelligence 145(1-2), 59–97 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  14. Iosifescu, M.: Finite Markov Chain and their Applications. Wiley, Chichester (1980)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

He, J., He, F., Dong, H. (2012). Pure Strategy or Mixed Strategy?. In: Hao, JK., Middendorf, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2012. Lecture Notes in Computer Science, vol 7245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29124-1_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29124-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29123-4

  • Online ISBN: 978-3-642-29124-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics