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Stochastic Search in Metaheuristics

In: Handbook of Metaheuristics

Author

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  • Walter J. Gutjahr

    (University of Vienna)

Abstract

Stochastic search is a key mechanism underlying many metaheuristics. The chapter starts with the presentation of a general framework algorithm in the form of a stochastic search process that contains a large variety of familiar metaheuristic techniques as special cases. Based on this unified view, questions concerning convergence and runtime are discussed on the level of a theoretical analysis. Concrete examples from diverse metaheuristic fields are given. In connection with runtime results, important topics as instance difficulty, phase transitions, parameter choice, No-Free-Lunch theorems, or fitness landscape analysis are addressed. Furthermore, a short sketch of the theory of black-box optimization is given, and generalizations of results to stochastic search under noise are outlined.

Suggested Citation

  • Walter J. Gutjahr, 2010. "Stochastic Search in Metaheuristics," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 573-597, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_19
    DOI: 10.1007/978-1-4419-1665-5_19
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    Cited by:

    1. ARNOLD, Florian & SÖRENSEN, Kenneth, 2017. "A simple, deterministic, and efficient knowledge-driven heuristic for the vehicle routing problem," Working Papers 2017012, University of Antwerp, Faculty of Business and Economics.
    2. Immanuel M. Bomze, 2018. "Building a completely positive factorization," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(2), pages 287-305, June.

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