Stochastic Search in Metaheuristics

Walter J. Gutjahr () and Roberto Montemanni ()
Additional contact information
Walter J. Gutjahr: University of Vienna
Roberto Montemanni: University of Applied Sciences of Southern Switzerland

Chapter Chapter 16 in Handbook of Metaheuristics, 2019, pp 513-540 from Springer

Abstract: 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.

Keywords: Fitness Landscape Analysis; First Hitting Time (FHT); Estimation-of-distribution Algorithms (EDAs); Stochastic Metaheuristics; OneMax (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-319-91086-4_16

Ordering information: This item can be ordered from
http://www.springer.com/9783319910864

DOI: 10.1007/978-3-319-91086-4_16

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().