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Stochastic Search in Metaheuristics

In: Handbook of Metaheuristics

Author

Listed:
  • Walter J. Gutjahr

    (University of Vienna)

  • Roberto Montemanni

    (University of Applied Sciences of Southern Switzerland)

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 at the level of a theoretical analysis. Concrete examples from diverse metaheuristic fields are given. In connection with runtime results, important topics such 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 and to robust optimization are outlined.

Suggested Citation

  • Walter J. Gutjahr & Roberto Montemanni, 2019. "Stochastic Search in Metaheuristics," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 513-540, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-91086-4_16
    DOI: 10.1007/978-3-319-91086-4_16
    as

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