Genetic Algorithms:“Non-Smooth” Discrete Optimization

  • Hung T. Nguyen
  • Vladik Kreinovich
Part of the Theory and Decision Library book series (TDLB, volume 38)


In Lesson 7, we described an algorithm (called simulated annealing) that solves “almost smooth” discrete optimization problems, i.e., problems in which a “small” change in the point x leads to a small change in the value of the objective function J(x). In this lesson, we consider “non-smooth” discrete optimization problems. For such problems, a different class of algorithms has been developed: genetic algorithms that simulate evolution in nature.


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  1. ▪.
    D. E. Goldberg, “Genetic and Evolutionary Algorithms Come of Age” (Communications of the ACM, March 1994, Vol. 37, No. 3, pp. 113–119 )CrossRefGoogle Scholar
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    D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning ( Addison-Wesley, Reading, MA, 1989 ).zbMATHGoogle Scholar
  3. ▪.
    Y. Davidor, Genetic algorithms and robotics: A heuristic strategy for optimization ( World Scientific, Singapore, 1991 ).zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Hung T. Nguyen
    • 1
  • Vladik Kreinovich
    • 2
  1. 1.New Mexico State UniversityLas CrucesUSA
  2. 2.University of Texas El PasoEl PasoUSA

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