Journal of Global Optimization

, Volume 19, Issue 3, pp 265–289 | Cite as

Reliability and Performance of UEGO, a Clustering-based Global Optimizer

  • Pilar M. Ortigosa
  • I. García
  • Márk Jelasity
Article

Abstract

UEGO is a general clustering technique capable of accelerating and/or parallelizing existing search methods. UEGO is an abstraction of GAS, a genetic algorithm (GA) with subpopulation support, so the niching (i.e. clustering) technique of GAS can be applied along with any kind of optimizers, not only genetic algorithm. The aim of this paper is to analyze the behavior of the algorithm as a function of different parameter settings and types of functions and to examine its reliability with the help of Csendes' method. Comparisons to other methods are also presented.

Global optimization Stochastic optimization Evolutionary algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beasley, D., Bull, D.R. and Martin, R.R. (1993), A Sequential Niche Technique for Multimodal Function Optimization. Evolutionary Computation 1(2): 101-125.Google Scholar
  2. Csendes, T. (1988), Nonlinear parameter estimation by global optimization-efficiency and reliability. Acta Cybernetica 8: 361-370.Google Scholar
  3. Deb, K. (1989), Genetic algorithms in multimodal function optimization. TCGA report no. 89002, The University of Alabama, Dept. of Engineering Mechanics.Google Scholar
  4. Deb, K. and Goldberg, D.E. (1989), An investigation of niche and species formation in genetic function optimization. In: Schaffer, J.D. (ed.) The Proceedings of the Third International Conference on Genetic Algorithms.Google Scholar
  5. Dixon, L. and Szego, G. (1975), Towards Global Optimization. North-Holland.Google Scholar
  6. Grefenstette, J.J. (1990), A User's Guide to GENESIS. Version 5.0. John J. Grefenstette.Google Scholar
  7. Hooker, J.N. (1995), Testing Heuristics: We Have It All Wrong. Journal of Heuristics 1(1): 33-42.Google Scholar
  8. Jelasity, M. (1998), UEGO, an Abstract Niching Technique for Global Optimization. In: Eiben, A.E., Bäck, T., Schoenauer, M. and Schwefel, H.-P. (eds), Parallel Problem Solving from Nature-PPSN V, Vol. 1498 of Lecture Notes in Computational Science. pp. 378-387.Google Scholar
  9. Jelasity, M. and Dombi, J. (1998), GAS, a Concept on Modeling Species in Genetic Algorithms. Artificial Intelligence 99(1): 1-19.Google Scholar
  10. Orvosh, D. and Davis, L. (1993), Shall we repair? Genetic algorithms, combinatorial optimizations and feasibility constraints. In: Forrest, S. (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms.Google Scholar
  11. Solis, F.J. and Wets, R.J.-B. (1981), Minimization by Random Search Techniques. Mathematics of Operations Research 6(1): 19-30.Google Scholar
  12. Törn, A. and Žilinskas, A. (1989), Global Optimization, Vol. 350 of Lecture Notes in Computational Science. Springer-Verlag, Berlin.Google Scholar
  13. Walster, G., Hansen, E. and Sengupta, S. (1985), Test results for global optimization algorithm. SIAM Numerical Optimization 1984 pp. 972-287.Google Scholar
  14. Wolpert, D.H. and Macready, W.G. (1997), No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1(1): 67-82.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Pilar M. Ortigosa
    • 1
  • I. García
    • 1
  • Márk Jelasity
    • 2
  1. 1.Computer Architecture & Electronics DepartmentUniversity of Almería, Cta. Sacramento SNAlmeríaSpain
  2. 2.Research Group on Artificial Intelligence MTA-JATESzegedHungary

Personalised recommendations