Skip to main content
SpringerLink
Log in

A hybrid immigrants scheme for genetic algorithms in dynamic environments

  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, USA, 1989.

    MATH  Google Scholar 

  2. J. H. Holland. Adaptation in Natural and Artificial Systems, Ann Arbor, University of Michigan Press, 1975.

    Google Scholar 

  3. D. E. Goldberg, R. E. Smith. Nonstationary Function Optimization Using Genetic Algorithms with Dominance and Diploidy. In Proceedings of the 2nd International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Mahwah, NJ, USA, pp. 59–68, 1987.

    Google Scholar 

  4. J. Branke. Evolutionary Optimization in Dynamic Environments, Kluwer Academic Publishers, Boston, MA, 2001.

    Google Scholar 

  5. R. W. Morrison. Designing Evolutionary Algorithms for Dynamic Environments, Springer-Verlag, Berlin Heidelberg, 2004.

    MATH  Google Scholar 

  6. K. Weicker. Evolutionary Algorithms and Dynamic Optimization Problems, Osnabrück, Germany, Derandere Verlag, 2003.

    Google Scholar 

  7. S. Yang, Y. S. Ong, Y. Jin (eds.), Evolutionary Computation in Dynamic and Uncertain Environments, Springer-Verlag, Berlin Heidelberg, March 2007.

    MATH  Google Scholar 

  8. Y. Jin, J. Branke. Evolutionary Optimization in Uncertain Environments: A Survey. IEEE Transactions on Evolutionary Computation, vol. 9, no. 3, pp. 303–317, 2005.

    Article  Google Scholar 

  9. J. J. Grefenstette. Genetic Algorithms for Changing Environments. Parallel Problem Solving from Nature, Elsevier Science Publishers, The Netherlands, vol.2, pp. 137–144, 1992.

    Google Scholar 

  10. F. Vavak, T. C. Fogarty. A Comparative Study of Steady State and Generational Genetic Algorithms for Use in Nonstationary Environments. In Proceedings of AISB Workshop on Evolutionary Computing, Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, vol. 1143, pp. 297–304, 1996.

    Google Scholar 

  11. H. G. Cobb, J. J. Grefenstette. Genetic Algorithms for Tracking Changing Environments. In Proceedings of the 5th International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, San Francisco, CA, USA, pp. 523–530, 1993.

    Google Scholar 

  12. R. W. Morrison, K. A. De Jong. Triggered Hypermutation Revisited. In Proceedings of the 2000 IEEE Congress on Evolutionary Computation, IEEE, pp. 1025–1032, 2000.

  13. C. N. Bendtsen, T. Krink. Dynamic Memory Model for Non-stationary Optimization. In Proceedings of the 2002 Congress on Evolutionary Computation, IEEE, pp. 145–150, 2002.

  14. J. Branke. Memory Enhanced Evolutionary Algorithms for Changing Optimization Problems. In Proceedings of the 1999 Congress on Evolutionary Computation, IEEE, vol. 3, pp. 1875–1882, 1999.

  15. A. Simões, E. Costa. An Immune System-based Genetic Algorithm to Deal with Dynamic Environments: Diversity and Memory. In Proceedings of the 6th International Conference on Artificial Neural Networks and Genetic Algorithms, Springer, pp. 168–174, 2003.

  16. K. Trojanowski, Z. Michalewicz. Searching for Optima in Non-stationary Environments. In Proceedings of the 1999 Congress on Evolutionary Computation, IEEE, pp. 1843–1850, 1999.

  17. S. Yang. Memory-based Immigrants for Genetic Algorithms in Dynamic Environments. In Proceedings of the 2005 Genetic and Evolutionary Computation Conference, vol. 2, pp. 1115–1122, 2005.

    Article  Google Scholar 

  18. S. Yang. Associative Memory Scheme for Genetic Algorithms in Dynamic Environments. Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 3907, Springer-Verlag, Berlin Heidelberg, pp. 788–799, 2006.

    Chapter  Google Scholar 

  19. J. Branke, T. Kaußler, C. Schmidth, H. Schmeck. A Multipopulation Approach to Dynamic Optimization Problems. In Proceedings of the Adaptive Computing in Design and Manufacturing, pp. 299–308, 2000.

  20. D. Parrott, X. Li. Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation. IEEE Transactions on Evolutionary Computation, vol. 10, no. 4, pp. 444–458, 2006.

    Article  Google Scholar 

  21. S. Yang. Genetic Algorithms with Elitism-based Immigrants for Changing Optimization Problems. Applications of Evolutionary Computing, Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, vol. 4448, pp. 627–636, 2007.

    Chapter  Google Scholar 

  22. S. Yang. Non-stationary Problem Optimization Using the Primal-dual Genetic Algorithm. In Proceedings of the 2003 IEEE Congress on Evolutionary Computation, IEEE, vol. 3, pp. 2246–253, 2003.

  23. S. Yang, X. Yao. Experimental Study on Population-based Incremental Learning Algorithms for Dynamic Optimization Problems. Soft Computing, vol. 9, no. 11, pp. 815–834, 2005.

    Article  MATH  Google Scholar 

  24. W. Cedeno, V. R. Vemuri. On the Use of Niching for Dynamic Landscapes. In Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, IEEE, pp. 361–366, 1997.

  25. N. Mori, H. Kita, Y. Nishikawa. Adaptation to Changing Environments by Means of the Memory Based Thermodynamical Genetic Algorithm. In Proceedings of the 7th International Conference on Genetic Algorithms, T. Bäck (ed.), Morgan Kaufmann Publishers, pp. 299–306, 1997.

  26. N. Mori, H. Kita, Y. Nishikawa. Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm. Parallel Problem Solving from Nature V, Lecture Notes in Computer Science, Springer-Verlag Berlin Heidelberg, vol. 1498, pp. 149–158, 1998.

    Chapter  Google Scholar 

  27. R. W. Morrison, K. A. De Jong. A Test Problem Generator for Non-stationary Environments. In Proceedings of the 1999 Congress on Evolutionary Computation, IEEE, vol. 3, pp. 2047–2053, 1999.

    Article  Google Scholar 

  28. M. Mitchell, S. Forrest, J. H. Holland. The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance. In Proceedings of the 1st European Conference on Artificial Life, MIT Press, Cambridge MA, USA, pp. 245–254, 1991.

    Google Scholar 

  29. D. E. Goldberg. The Design of Innovation: Lessons from and for Competent Genetic Algorithms, Kluwer Academic Publishers, Boston, MA, 2002.

    MATH  Google Scholar 

  30. L. D. Whitley. Fundamental Principles of Deception in Genetic Search. Foundations of Genetic Algorithms I, Morgan Kaufmann Publishers, pp. 221–241, 1991.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Leicester, Leicester, LE1 7RH, UK

    Shengxiang Yang

  2. Department of Physics and Mathematics, FFCLRP, University of São Paulo, Ribeirão Preto, 14040-901, Brazil

    Renato Tinós

Authors
  1. Shengxiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Renato Tinós
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shengxiang Yang.

Additional information

This work was supported by UK EPSRC (No. EP/E060722/01) and Brazil FAPESP (Proc. 04/04289-6).

Shengxiang Yang received the B.Sc. and M.Sc. degrees in automatic control and the Ph.D. degree in systems engineering from Northeastern University, P. R. China in 1993, 1996 and 1999, respectively. He was a postdoctoral research associate in the Department of Computer Science, King’s College London from October 1999 to October 2000. He is currently a lecturer in the Department of Computer Science at University of Leicester, UK.

He has published over 50 papers in books, journals and conferences. He has co-guest-edited a special issue for the journal of Genetic Programming and Evolvable Machines and has co-edited a book Evolutionary Computation in Dynamic and Uncertain Environments, published in March 2007. His current research interests include evolutionary and genetic algorithms, artificial neural networks for combinatorial optimization problems, scheduling problems, dynamic optimization problems, and network flow problems and algorithms.

Dr. Yang serves as the area editor, associate editor and member of editorial board of three international journals. He is a member of the Working Group on Evolutionary Computation in Dynamic and Uncertain Environments, Evolutionary Computation Technical Committee, IEEE Computational Intelligence Society (CIS). He is a member of IEEE and ACM SIGEVO.

Renato Tinós received the B.Sc. degree in electrical engineering from State University of São Paulo (UNESP), Brazil in 1994, and the M.Sc. and Ph.D. degrees in electrical engineering from the University of São Paulo (USP) at São Carlos, Brazil in 1999 and 2003, respectively. He then joined the Department of Computer Science of USP at São Carlos as a research scientist. He is currently an assistant professor at the Department of Physics and Mathematics of USP at Ribeirão Preto.

His research interests include evolutionary algorithms, dynamic optimization, robotics, fault tolerance, and neural networks.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, S., Tinós, R. A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int J Automat Comput 4, 243–254 (2007). https://doi.org/10.1007/s11633-007-0243-9

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-007-0243-9

Keywords

Search

Navigation