Skip to main content
SpringerLink
Log in
Book cover

International Conference in Swarm Intelligence

ICSI 2015: Advances in Swarm and Computational Intelligence pp 29–37Cite as

A Self-configuring Metaheuristic for Control of Multi-Strategy Evolutionary Search

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9142))

Abstract

There exists a great variety of evolutionary algorithms (EAs) that represent different search strategies for many classes of optimization problems. Real-world problems may combine several optimization features that are not known beforehand, thus there is no information about what EA to choose and which EA settings to apply. This study presents a novel metaheuristic for designing a multi-strategy genetic algorithm (GA) based on a hybrid of the island model, cooperative and competitive coevolution schemes. The approach controls interactions of GAs and leads to the self-configuring solving of problems with a priori unknown structure. Two examples of implementations of the approach for multi-objective and non-stationary optimization are discussed. The results of numerical experiments for benchmark problems from CEC competitions are presented. The proposed approach has demonstrated efficiency comparable with other well-studied techniques. And it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Holland, J.: Adaptation In Natural and Artificial Systems. University of Michigan Press (1975)

    Google Scholar 

  2. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Reading. Addison-Wesley, MA (1989)

    Google Scholar 

  3. Schaefer, R., Cotta, C., Kołodziej, J.: Parallel problem solving from nature. In: Proc. PPSN XI 11th International Conference, Kraków, Poland (2010)

    Google Scholar 

  4. Back, T.: Self-adaptation in genetic algorithms. In: Proceedings of 1st European Conference on Articial Life (1992)

    Google Scholar 

  5. Eiben, A.E., Hintering, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3(2) (1999)

    Google Scholar 

  6. Lee, M., Takagi, H.: Dynamic control of genetic algorithms using fuzzy logic techniques. In: Proceedings of the Fifth International Conference on Genetic Algorithms (1993)

    Google Scholar 

  7. Liu, J., Lampinen, J.: A fuzzy adaptive differential evolution algorithm. Soft Comput.: Fusion Found., Methodologies Applicat. 9(6) (2005)

    Google Scholar 

  8. Ficici, S.G.: Solution Concepts in Coevolutionary Algorithms. A Doctor of Philosophy Dissertation, Brandeis University (2004)

    Google Scholar 

  9. Mühlenbein, H.: Strategy adaptation by competing subpopulations. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 199–208. Springer, Heidelberg (1994)

    Google Scholar 

  10. Potter, M.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866. Springer, Heidelberg (1994)

    Google Scholar 

  11. Mallipeddi, R., Suganthan, P.N.: Ensemble differential evolution algorithm for CEC2011 problems. In: IEEE Congress on Evolutionary Computation, New Orleans, USA (2011)

    Google Scholar 

  12. Sergienko, R.B., Semenkin, E.S.: Competitive cooperation for strategy adaptation in coevolutionary genetic algorithm for constrained optimization. In: Proc. of 2010 IEEE Congress on Evolutionary Computation (2010)

    Google Scholar 

  13. Peng, F., Tang, K., Chen, G., Yao, X.: Population-based algorithm portfolios for numerical optimization. IEEE Trans. Evol. Comput. 14 (5) (2010)

    Google Scholar 

  14. Semenkin, E., Semenkina, M.: Self-configuring genetic algorithm with modified uniform crossover operator. In: Tan, Y., Shi, Y., Ji, Z. (eds.) ICSI 2012, Part I. LNCS, vol. 7331, pp. 414–421. Springer, Heidelberg (2012)

    Google Scholar 

  15. Zhoua, A., Qub, B.-Y., Lic, H., Zhaob, S.-Zh., Suganthanb, P.N., Zhangd, Q.: Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 1(1) (2011)

    Google Scholar 

  16. Zhang, Q., Zhou, A., Zhao, Sh., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the CEC 2009 special session and competition. In: IEEE Congress on Evolutionary Computation, IEEE CEC 2009, Norway (2009)

    Google Scholar 

  17. Sopov, E., Ivanov, I.: Design efficient technologies for context image analysis in dialog HCI using self-configuring novelty search genetic algorithm. In: Proc. of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2014), Vienna, Austria (2014)

    Google Scholar 

  18. Sopov, E., Panfilov, I.: Intrusion detectors design with self-configuring multi-objective genetic algorithm. In: Proc. of 2014 International Conference on Network Security and Communication Engineering (NSCE2014), Hong Kong (2014)

    Google Scholar 

  19. Sopov, E., Panfilov, I.: Self-tuning SVM with feature selection for text categorization problem. In: Proc. of International Conference on Computer Science and Artificial Intelligence (ICCSAI2014), Wuhan, China (2014)

    Google Scholar 

  20. Nguyena, T.T., Yang, S., Branke, J.: Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation 6 (2012)

    Google Scholar 

  21. Morrison, R.W., De Jong, K.A.: A test problem generator for non-stationary environments. In: Proc. of the 1999 Congr. on Evol. Comput. (1999)

    Google Scholar 

  22. Li, C., Yang, S., Nguyen, T.T., Yu, E.L., Yao, X., et al.: Benchmark Generator for CEC2009 Competition on Dynamic Optimization. Technical Report 2008, Department of Computer Science, University of Leicester, UK (2008)

    Google Scholar 

  23. Brest, J., Zamuda, A., Boskovic, B., Maucec, M.S., Zumer, V.: Dynamic optimization using self-adaptive differential evolution. In: Proc. of IEEE Congr. Evol. Comput. (2009)

    Google Scholar 

  24. Li, C., Yang, S.: A clustering particle swarm optimizer for dynamic optimization. In: Proc. of the Congr. on Evol. Comput. (2009)

    Google Scholar 

  25. Yu, E.L., Suganthan, P.N.: Evolutionary programming with ensemble of external memories for dynamic optimization. In: Proc. of IEEE Congr. Evol. Comput. (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Siberian State Aerospace University, Krasnoyarsk, Russia

    Evgenii Sopov

Authors
  1. Evgenii Sopov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Evgenii Sopov .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Universidade de Pernambuco, Recife, Brazil

    Fernando Buarque

  4. Instituto Politécnico Nacional, Mexico, Mexico

    Alexander Gelbukh

  5. Indian Statistical Institute, Kolkata, India

    Swagatam Das

  6. University of Pretoria, Pretoria, South Africa

    Andries Engelbrecht

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Sopov, E. (2015). A Self-configuring Metaheuristic for Control of Multi-Strategy Evolutionary Search. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9142. Springer, Cham. https://doi.org/10.1007/978-3-319-20469-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20469-7_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20468-0

  • Online ISBN: 978-3-319-20469-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation