Skip to main content

Global Optimization of Multimodal Deceptive Functions

  • Conference paper
  • 1215 Accesses

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8600)

Abstract

Local search algorithms operating in high-dimensional and multimodal search spaces often suffer from getting trapped in a local optima, therefore requiring many restarts. Even with multiple restarts, their search efficiency critically depends on the choice of the neighborhood structure. In this paper we propose an approach in which the need for the restarts is exploited to improve the neighborhood definitions. Namely, a graph clustering based linkage detection method is used to mine the information from several runs, in order to extract variable dependencies and update the neighborhood structure, variation operators accordingly. We show that the adaptive neighborhood structure approach enables the efficient solving of challenging global optimization problems that are both deceptive and multimodal.

Keywords

  • Simulated Annealing
  • Neighborhood Structure
  • Graph Cluster
  • Quantum Annealing
  • Perturbation Probability

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: 10.1007/978-3-662-44320-0_13
  • Chapter length: 12 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   44.99
Price excludes VAT (USA)
  • ISBN: 978-3-662-44320-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   59.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Pelikan, M.: Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms. Springer (2005)

    Google Scholar 

  2. Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Norwell (2002)

    CrossRef  Google Scholar 

  3. Goldberg, D.E., Deb, K., Kargupta, H., Harik, G.: Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In: Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, CA, pp. 56–64. Morgan Kaufman (1993)

    Google Scholar 

  4. Harik, G.R., Goldberg, D.E.: Learning linkage. In: Belew, R.K., Vose, M.D. (eds.) FOGA, pp. 247–262. Morgan Kaufmann (1996)

    Google Scholar 

  5. Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE-EC 3(4), 287 (1999)

    Google Scholar 

  6. Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: The Bayesian optimization algorithm. In: Banzhaf, W., et al. (eds.) GECCO 1999, Orlando, FL, July 13-17, vol. I, pp. 525–532. Morgan Kaufmann Publishers, San Fransisco (1999)

    Google Scholar 

  7. Watson, R.A., Pollack, J.: A computational model of symbiotic composition in evolutionary transitions. Biosystems 69(2-3), 187–209 (2003), Special Issue on Evolvability, ed. Nehaniv

    Google Scholar 

  8. de Jong, E.D.: Representation Development from Pareto-Coevolution. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 262–273. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

  9. Toussaint, M.: Compact Genetic Codes as a Search Strategy of Evolutionary Processes. In: Wright, A.H., Vose, M.D., De Jong, K.A., Schmitt, L.M. (eds.) FOGA 2005. LNCS, vol. 3469, pp. 75–94. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  10. de Jong, E.D., Thierens, D., Watson, R.A.: Hierarchical genetic algorithms. In: Yao, X., et al. (eds.) PPSN VIII. LNCS, vol. 3242, pp. 232–241. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  11. Pelikan, M., Goldberg, D.E.: Escaping hierarchical traps with competent genetic algorithms. In: Spector, L., et al. (eds.) GECCO 2001, July 7-11, pp. 511–518. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  12. Yu, T.L., Goldberg, D.E.: Conquering hierarchical difficulty by explicit chunking: substructural chromosome compression. In: GECCO 2006, pp. 1385–1392. ACM Press, NY (2006)

    Google Scholar 

  13. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    CrossRef  MATH  MathSciNet  Google Scholar 

  14. van Dongen, S.: Graph Clustering by Flow Simulation. PhD thesis, U. of Utrecht (2000)

    Google Scholar 

  15. Brohée, S., van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7, 488 (2006)

    CrossRef  Google Scholar 

  16. Iclănzan, D., Dumitrescu, D.: Graph clustering based model building. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 506–515. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  17. Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Whitley, L.D. (ed.) Foundations of Genetic Algorithms 2, San Mateo, pp. 93–108. Morgan Kaufmann (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Iclănzan, D. (2014). Global Optimization of Multimodal Deceptive Functions. In: Blum, C., Ochoa, G. (eds) Evolutionary Computation in Combinatorial Optimisation. EvoCOP 2014. Lecture Notes in Computer Science, vol 8600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44320-0_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-44320-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44319-4

  • Online ISBN: 978-3-662-44320-0

  • eBook Packages: Computer ScienceComputer Science (R0)