9783030077365 - advances in metaheuristics algorithms: methods and applications (studies in computational intelligence, band 775) von cuevas, erik; zaldívar, daniel; pérez-cisneros, marco (2 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 282 von 538 - Studies in Computational Intelligence
- Softcover
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Taschenbuch. Zustand: Neu. Advances in Metaheuristics Algorithms: Methods and Applications | Erik Cuevas (u. a.) | Taschenbuch | xiv | Englisch | 2018 | Springer | EAN 9783030077365 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter:… preigu.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing, 2018
Serie: Buch 282 von 538 - Studies in Computational Intelligence
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 106,99
EUR 61,80 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless… still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip thoseof the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
