EnglishStochastic 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.
Stochastic Search in Metaheuristics / Gutjahr Walter, J; Montemanni, Roberto. - (2019), pp. 513-540.
| Data di pubblicazione: | 2019 |
| Titolo: | Stochastic Search in Metaheuristics |
| Autore/i: | Gutjahr Walter, J; Montemanni, Roberto |
| Autore/i UNIMORE: | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/978-3-319-91086-4_16 |
| Codice identificativo Scopus: | 2-s2.0-85053799424 |
| Titolo del libro: | Handbook of Metaheuristics |
| Citazione: | Stochastic Search in Metaheuristics / Gutjahr Walter, J; Montemanni, Roberto. - (2019), pp. 513-540. |
| Tipologia | Capitolo/Saggio |
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