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Measures for Non-Stationary Optimization Tasks

  • Krzysztof Trojanowski
  • Andrzej Obuchowicz
Conference paper

Abstract

The aim of this paper is to study the problem of optimization of non-stationary problems with evolutionary algorithms. Obtained solutions have to satisfy different demands than with problems static in time, so the approach to this class of problems has to be different. In this paper we present a review of measures for the obtained results. Some new measures of optimization tool quality and the non-stationary problem difficulty are also proposed.

Keywords

Complete Lattice Hierarchical Relation Normed Vector Space Knowledge Module Filter Base 
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.

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References

  1. [1]
    Bäck, “On the Behaviour of Evolutionary Algorithms in Dynamic Environments”, ICEC’98, IEEE Publishing, 1998, pp 446–451.Google Scholar
  2. [2]
    Bäck, T., Schutz, M., “Intelligent Mutation Rate Control in Canonical Genetic Algorithm”, ISMIS’ 96, vol. 1079 in LNAI, Springer, 1996, pp 158–167.Google Scholar
  3. [3]
    Branke, J., “Memory Enhanced Evolutionary Algorithm for Changing Optimisation Problems”, CEC’99, IEEE Publishing, 1999, pp 1875–1882.Google Scholar
  4. [4]
    Bryson, A.E., Ho, C., “Applied Optimal Control”, New York: A halsted Press Book, 1975.Google Scholar
  5. [5]
    Cedeno, W., Vemuri, V., R., “On the Use of Niching for Dynamic Landscapes”, ICEC’97, IEEE Publishing, Inc., 1997, pp 361–366.Google Scholar
  6. [6]
    Cobb., H., G., Grefenstette, J., J., “Genetic Algorithms for Tracking Changing Environments”, V ICGA’93, Morgan Kauffman, 1993, pp 523–530.Google Scholar
  7. [7]
    Dasgupta, D., McGregor, D. R, “Non-Stationary Function Optimisation Using the Structured Genetic Algorithm”, 2PPSN: Parallel Problem Solving from Nature, Elsevier Science Publishers B. V., 1992, pp 145–154.Google Scholar
  8. [8]
    Galar, R., “Evolutionary search with soft selection”, Biological Cybernetics,Vol.60, 1989, pp.357–364.MathSciNetCrossRefMATHGoogle Scholar
  9. [9]
    Goldberg, D., E., Smith, R, E., “Nonstationary Function Optimisation Using Genetic Algorithms with Dominance and Diploidy”, II ICGA’87, Lawrence Erlbaum Associates, 1987, pp 59–68.Google Scholar
  10. [10]
    Grefenstette, J., J., “Genetic Algorithms for Changing Environments”, 2PPSN, Elsevier Science Publishers B. V., 1992, pp 137–144.Google Scholar
  11. [11]
    Korbicz, J., Obuchowicz, A., Patan, K, “Network of Dynamic Neurons in Fault Detection Systems”, Proc. of the IEEE Int. Conf. System, Man, Cybernetics, San Diego, USA, IEEE Publishing, 1998, pp. 1862–1867.Google Scholar
  12. [12]
    Mori, N., Kita, H., Nishikawa, Y., “Adaptation to a Changing Environments by Means of the Thermodynamical Genetic Algorithm”, 4PPSN, vol. 1141 in LNCS, Springer, 1996, pp 513–522.Google Scholar
  13. [13]
    Mori, N., Kita, H., Nishikawa, Y., “Adaptation to Changing Environments by Means of the Feedback Thermodynamical Genetic Algorithm”, 5PPSN, vol. 1498 in LNCS, Springer, 1998, pp 149–157.Google Scholar
  14. [14]
    Naudts, B., and Kallel, L., “A Comparison of Predictive Measures of Problem Difficulty in Evolutionary Algorithms”, IEEE TI-ansactions on Evolutionary Computation, Vol 4, No.1, 2000, pp 1–15.CrossRefGoogle Scholar
  15. [15]
    Ng, K, P., Wong, K, C., “A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimisation”, VI ICGA’95, Morgan Kauffman, 1995, pp 159–166.Google Scholar
  16. [16]
    Obuchowicz, A., “Adaptation in time-varying landscape using an evolutionary search with soft election”, 3KAEiOG 1999, Warsaw University of Technology Press, pp.245-251.Google Scholar
  17. [17]
    Reeves, C.R, and Wright, C.C., “Epistasis in genetic algorithms: An experimental design perspective”, VI ICGA’95, Morgan Kauffman, 1995, pp 217–230.Google Scholar
  18. [18]
    Trojanowski, K, and Michalewicz, Z., “Searching for Optima in Non-Stationary Environments”, CEC’99, IEEE Publ., pp.1843-1850.Google Scholar
  19. [19]
    Vavak, F., Fogarty, T., C., “Comparison of Steady State and Generational Genetic Algorithm for Use in Nonstationary Environments” ICEC’96, IEEE Publ.Inc., 1996, pp 192–195.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Krzysztof Trojanowski
    • 1
  • Andrzej Obuchowicz
    • 2
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  2. 2.Institute of Control and Computation EngineeringTechnical University of Zielona GóraZielona GóraPoland

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