Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization
Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in h...
Bewaard in:
| Andere auteurs: | , |
|---|---|
| Formaat: | Elektronisch Hoofdstuk |
| Taal: | Engels |
| Gepubliceerd in: |
IntechOpen
2018
|
| Onderwerpen: | |
| Online toegang: | DOAB: download the publication DOAB: description of the publication |
| Tags: |
Voeg label toe
Geen labels, Wees de eerste die dit record labelt!
|
| Samenvatting: | Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems. |
|---|---|
| Fysieke beschrijving: | 1 electronic resource (70 p.) |
| ISBN: | intechopen.71401 9781789233292 9781789233285 9781838815721 |
| Toegang: | Open Access |