Skip to main content
SpringerLink
Log in

Immigrant schemes for evolutionary algorithms in dynamic environments: Adapting the replacement rate

  • Research Papers
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

One approach for evolutionary algorithms (EAs) to address dynamic optimization problems (DOPs) is to maintain diversity of the population via introducing immigrants. So far all immigrant schemes developed for EAs have used fixed replacement rates. This paper examines the impact of the replacement rate on the performance of EAs with immigrant schemes in dynamic environments, and proposes a self-adaptive mechanism for EAs with immigrant schemes to address DOPs. Our experimental study showed that the new approach could avoid the tedious work of fine-tuning the parameter and outperformed other immigrant schemes using a fixed replacement rate with traditionally suggested values in most cases.

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. Jin Y, Branke J. Evolutionary optimization in uncertain environments: A survey. IEEE Trans Evol Comput, 2005, 9: 303–317

    Article  Google Scholar 

  2. Rohlfshagen P, Yao X. Attributes of dynamic combinatorial optimisation. In: Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL’2008), Melbourne, Australia, 2008. 442–451

  3. Yu X, Tang K, Chen T, et al. Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization. Memetic Comput, 2009, 1: 3–24

    Article  Google Scholar 

  4. Yu X, Tang K, Yao X. An immigrants scheme based on environmental information for genetic algorithms in changing environments. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation, Hong Kong, China, 2008. 1141–1147

  5. Yang S. Genetic algorithms with elitism-based immigrants for changing optimization problems. In: Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing, Valencia, Spain, 2007. 627–636

  6. Yang S, Tinós R. A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int J Automat Comput, 2007, 4: 243–254

    Article  Google Scholar 

  7. Tinós R, Yang S. Genetic algorithms with self-organized criticality for dynamic optimization problems. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Edinburgh, UK, 2005. 2816–2823

  8. Hinterding R, Michalewicz Z, Eiben A E. Adaptation in evolutionary computation: A survey. In: Proceedings of the 4th International Conference on Evolutionary Computation, Indianapolis, USA, 1997. 65–69

  9. Shi J P, Zhang W G, Li G W, et al. Research on allocation efficiency of the redistributed pseudo inverse algorithm. Sci China Inf Sci, 2010, 53: 271–277

    Article  Google Scholar 

  10. Zhang J, Lo W L, Chung H. Pseudo-coevolutionary genetic algorithms for power electronics regulators optimization. IEEE Trans Syst Man Cybern C Appl Rev, 2006, 36: 590–598

    Article  Google Scholar 

  11. Zhang J, Chung H, Lo W L. Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Trans Evol Comput, 2007, 11: 326–335

    Article  Google Scholar 

  12. Yao X, Liu Y, Lin G. Evolutionary programming made faster. IEEE Trans Evol Comput, 1999, 3: 82–102

    Article  Google Scholar 

  13. Beyer H G. Toward a theory of evolution strategies: Self-adaptation. Evol Comput, 1996, 3: 311–347

    Article  MathSciNet  Google Scholar 

  14. Qin A K, Liang V L, Suganthan P N. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput, 2009, 13: 398–417

    Article  Google Scholar 

  15. Zhan Z H, Zhang J, Li Y, et al. Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern B Cybern, 2009, 39: 1362–1381

    Article  Google Scholar 

  16. Grefenstette J J. Genetic algorithms for changing environments. In: Proceedings of Parallel Problem Solving from Nature, Brussels, Belgium, 1992. 137–144

  17. Cobb H G, Grefenstette J J. Genetic algorithms for tracking changing environments. In: Proceedings of the 5th International Conference on Genetic Algorithms, San Diego, USA, 1993. 523–530

  18. Yang S. Memory-based immigrants for genetic algorithms in dynamic environments. In: Proceedings of the 2005 Genetic and Evolutionary Computation Conference, Washington DC, USA, 2005. 1115–1122

  19. Branke J. Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 IEEE Congress on Evolutionary Computation, Washington DC, USA, 1999. 1875–1882

  20. Cormen T H, Leiserson C E, Rivest R L, et al. Introduction to algorithms. 2nd ed. Cambridge: MIT Press and New York: McGraw-Hill, 2001

    MATH  Google Scholar 

  21. Aho A V, Hopcroft J E, Ullman J D. Data structures and algorithms. Reading: Addison-Wesley, 1983

    MATH  Google Scholar 

  22. Yang S. Non-stationary problem optimization using the primal-dual genetic algorithm. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation, Canberra, Australia, 2003. 2246–2253

  23. Yang S, Yao X. Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput, 2005, 9: 815–834

    Article  MATH  Google Scholar 

  24. Mitchell M, Forrest S, Holland J H. The royal road for genetic algorithms: Fitness landscapes and GA performance. In: Proceedings of the 1st European conference on artificial life, Paris, France, 1991. 245–254

Download references

Author information

Authors and Affiliations

  1. Nature Inspired Computation and Applications Laboratory, School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Xin Yu, Ke Tang & Xin Yao

  2. Centre of Excellence for Research in Computational Intelligence and Applications, School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Xin Yao

Authors
  1. Xin Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ke Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ke Tang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yu, X., Tang, K. & Yao, X. Immigrant schemes for evolutionary algorithms in dynamic environments: Adapting the replacement rate. Sci. China Inf. Sci. 54, 1352–1364 (2011). https://doi.org/10.1007/s11432-011-4211-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-011-4211-1

Keywords

Search

Navigation