Abstract
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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Reference
D. E. Goldberg, “Genetic and Evolutionary Algorithms Come of Age” (Communications of the ACM, March 1994, Vol. 37, No. 3, pp. 113–119 )
D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning ( Addison-Wesley, Reading, MA, 1989 ).
Y. Davidor, Genetic algorithms and robotics: A heuristic strategy for optimization ( World Scientific, Singapore, 1991 ).
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© 1997 Springer Science+Business Media Dordrecht
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Nguyen, H.T., Kreinovich, V. (1997). Genetic Algorithms:“Non-Smooth” Discrete Optimization. In: Applications of Continuous Mathematics to Computer Science. Theory and Decision Library, vol 38. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0743-5_8
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DOI: https://doi.org/10.1007/978-94-017-0743-5_8
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4901-8
Online ISBN: 978-94-017-0743-5
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