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Stochastic Search in Metaheuristics

  • Walter J. Gutjahr
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 146)

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.

Keywords

Particle Swarm Optimization Simulated Annealing Variable Neighborhood Search Fitness Landscape Metaheuristic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.University of ViennaViennaAustria

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