Skip to main content

A Memetic Framework for Solving Difficult Inverse Problems

  • Conference paper
  • First Online:
Applications of Evolutionary Computation (EvoApplications 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8602))

Included in the following conference series:

  • 1825 Accesses

Abstract

The paper introduces a multi-deme, memetic global optimization strategy Hierarchic memetic Strategy (HMS) especially well-suited to the solution of a class of parametric inverse problems. This strategy develops dynamically a tree of dependent populations (demes) searching with the various accuracy growing from the root to the leaves. The search accuracy is associated with the accuracy of solving direct problems by \(hp\)–adaptive Finite Element Method. Throughout the paper we describe details of exploited accuracy adaptation and computational cost reduction mechanisms, an agent-based architecture of the proposed system, a sample implementation and preliminary benchmark results.

The work presented in this paper has been partially supported by Polish National Science Center grants no. DEC-2012/07/B/ST6/01229 and DEC-2011/03/B/ST6/01393.

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

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. Tarantola, A.: Inverse Problem Theory. Mathematics and its Applications. Society for Industrial and Applied Mathematics (2005)

    Google Scholar 

  2. Engl, H., Hanke, M., Neubauer, A.: Regularization of Inverse Problems. Mathematics and its Applications, vol. 375. Springer, Heidelberg (1996)

    Google Scholar 

  3. Pardalos, P., Romeijn, H.: Handbook of Global Optimization (Nonconvex Optimization and its Applications), vol. 2. Kluwer (1995)

    Google Scholar 

  4. Chakraborty, U.K. (ed.): Advances in Differential Evolution, vol. 143. Studies in Computational Intelligence. Springer (2008)

    Google Scholar 

  5. Cantú Paz, E.: Efficient and accurate parallel genetic algorithms, vol. 2. Kluwer (2000)

    Google Scholar 

  6. Törn, A.A.: A search clustering approach to global optimization. In: Dixon, L.C.W., Szegö, G.P. (eds.) Towards Global Optimisation 2, pp. 49–62. North-Holland, Amsterdam (1978)

    Google Scholar 

  7. Schaefer, R., Adamska, K., Telega, H.: Genetic clustering in continuous landscape exploration. Engineering Applications of Artificial Intelligence 17, 407–416 (2004)

    Article  Google Scholar 

  8. Schaefer, R., Kołodziej, J.: Genetic search reinforced by the population hierarchy. In: Foundations of Genetic Algorithms 7, pp. 383–399, Morgan Kaufman (2003)

    Google Scholar 

  9. Wierzba, B., Semczuk, A., Kołodziej, J., Schaefer, R.: Hierarchical Genetic Strategy with real number encoding. In: Proceedings of the 6th Conference on Evolutionary Algorithms and Global Optimization, pp. 231–237 (2003)

    Google Scholar 

  10. Barabasz, B., Migórski, S., Schaefer, R., Paszyński, M.: Multi-deme, twin adaptive strategy hp-HGS. Inverse Problems in Science and Engineering 19(1), 3–16 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  11. Demkowicz, L., Kurtz, J., Pardo, D., Paszyński, M., Rachowicz, W., Zdunek, A.: Computing with hp Finite Elements II. Frontiers: Three-Dimensional Elliptic and Maxwell Problems with Applications. Chapman & Hall/CRC (2007)

    Google Scholar 

  12. Neri, F., Cotta, C., Moscato, P. (eds.): Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol. 379. Springer, Heidelberg (2012)

    Google Scholar 

  13. Cetnarowicz, K., Kisiel-Dorohinicki, M., Nawarecki, E.: The application of evolution process in multi-agent world (MAW) to the prediction system. In: Tokoro, M. (ed.) Proceedings of the 2nd International Conference on Multiagent Systems (ICMAS 1996). AAAI Press (1996)

    Google Scholar 

  14. Byrski, A., Schaefer, R., Smołka, M., Cotta, C.: Asymptotic guarantee of success for multi-agent memetic systems. Bulletin of the Polish Academy of Sciences: Technical Sciences 61(1), 257–278 (2013)

    Google Scholar 

  15. Jojczyk, P., Schaefer, R.: Global impact balancing in the hierarchic genetic search. Computing and Informatics 28(2), 181–193 (2009)

    Google Scholar 

  16. Wróbel, K., Torba, P., Paszyński, M., Byrski, A.: Evolutionary multi-agent computing in inverse problems. Computer Science 14(3), 367–383 (2013)

    Article  Google Scholar 

  17. Burczyński, T., Orantek, P.: The hybrid genetic-gradient algorithm. In: Proceedings of 3rd KAEGiOG Conference, Potok Złoty, Poland (1999)

    Google Scholar 

  18. Grochowski, M., Smołka, M., Schaefer, R.: Architectural principles and scheduling strategies for computing agent systems. Fundamenta Informaticae 71(1), 15–26 (2006)

    MATH  MathSciNet  Google Scholar 

  19. Bellifemine, F.L., Caire, G., Greenwood, D.: Developing Multi-Agent Systems with JADE. Wiley (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maciej Smołka .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Smołka, M., Schaefer, R. (2014). A Memetic Framework for Solving Difficult Inverse Problems. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-45523-4_12

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45522-7

  • Online ISBN: 978-3-662-45523-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics