Skip to main content

An Improved Primal-Dual Genetic Algorithm for Optimization in Dynamic Environments

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 4234)

Abstract

Inspired by the complementary and dominance mechanism in nature, the Primal-Dual Genetic Algorithm (PDGA) has been proved successful in dynamic environments. In this paper, an important operator in PDGA, primal-dual mapping, is discussed and a new statistics-based primal-dual mapping scheme is proposed. The experimental results on the dynamic optimization problems generated from a set of stationary benchmark problems show that the improved PDGA has stronger adaptability and robustness than the original for dynamic optimization problems.

Keywords

  • Genetic Algorithm
  • Dynamic Environment
  • Fitness Landscape
  • Distance Space
  • Dynamic Optimization Problem

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.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/11893295_92
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   139.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-46485-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environment. Technical Report AIC-90-001, Naval Research Laboratory, Washington, USA (1990)

    Google Scholar 

  2. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Maenner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature 2, pp. 137–144. North-Holland, Amsterdam (1992)

    Google Scholar 

  3. Lewis, J., Hart, E., Ritchie, G.: A Comparison of Dominance Mechanisms and Simple Mutation on Non-Stationary Problems. In: Eiben, A.E., Back, T., Schoenauer, M., Schwefel, H.P. (eds.) Proceeding of the 5th Int. Conf. on Parallel Problem Solving from Nature, pp. 139–148 (1998)

    Google Scholar 

  4. Yang, S.: The Primal-Dual Genetic algorithm. In: Proc. of the 3rd Int. Conf. on Hybrid Intelligent System, IOS Press, Amsterdam (2003)

    Google Scholar 

  5. Yang, S.: Non-stationary problem optimization using the primal-dual genetic algorithm. In: Proceeding of the 2003 Congress on Evolutionary Computation, pp. 2246–2253 (2003)

    Google Scholar 

  6. Goldberg, D.E., Smith, R.E.: Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: Grefenstette, J.J. (ed.) Second International Conference on Genetic Algorithms, pp. 59–68. Lawrence Erlbaum Associates, Mahwah (1987)

    Google Scholar 

  7. Smith, R.E.: Diploid genetic algorithms for search in time varying environments. In: Annual Southeast Regional Conference of the ACM, New York, pp. 175–179 (1987)

    Google Scholar 

  8. Hadad, B.S., Eick, C.F.: Supporting polyploidy in genetic algorithms using dominance vectors. In: Angeline, P.J., Reynolds, R.G., Mcdonnell, J.R., Eberhart, R. (eds.) Proceedings of the Sixth International Conference on Evolutionary Programming, pp. 223–234 (1997)

    Google Scholar 

  9. Ryan, C.: Diploidy without dominance. In: Alander, J.T. (ed.) Third Nordic Workshop on Genetic Algorithms, pp. 63–70 (1997)

    Google Scholar 

  10. Lewis, J., Hart, E., Ritchie, G.: A comparison of dominance mechanisms and simple mutation on non-stationary problems. In: Eiben, A.E., Back, T., Schoenauer, M., Schwefel, H.P. (eds.) Parallel Problem Solving from Nature, pp. 139–148 (1998)

    Google Scholar 

  11. Collard, P., Escazut, C., Gaspar, A.: An evolutionary approach for time dependant optimization. International Journal on Artificial Intelligence Tools 6, 665–695 (1997)

    CrossRef  Google Scholar 

  12. Mitchell, M., Forest, S., Holland, J.H.: The Royal Road for Genetic Algorithms: Fitness Landscape and GA performance. In: Varela, F.J., Bourgine, P. (eds.) Proceeding of 1st European Conference on Artificial Life, pp. 245–254. MIT Press, Cambridge (1992)

    Google Scholar 

  13. Goldberg, D.E.: Genetic Algorithms and Walsh Function: Part I, a Gentle Introduction. Complex System, 129–152 (1989)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, H., Wang, D. (2006). An Improved Primal-Dual Genetic Algorithm for Optimization in Dynamic Environments. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_92

Download citation

  • DOI: https://doi.org/10.1007/11893295_92

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)