Sprache: Englisch
Verlag: Springer International Publishing, 2018
ISBN 10: 3319877127 ISBN 13: 9783319877129
Anbieter: moluna, Greven, Deutschland
EUR 92,27
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Taschenbuch. Zustand: Neu. NEO 2016 | Results of the Numerical and Evolutionary Optimization Workshop NEO 2016 and the NEO Cities 2016 Workshop held on September 20-24, 2016 in Tlalnepantla, Mexico | Yazmin Maldonado (u. a.) | Taschenbuch | xiii | Englisch | 2018 | Springer | EAN 9783319877129 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 160,59
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. reprint edition. 296 pages. 9.25x6.10x0.67 inches. In Stock.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing, 2018
ISBN 10: 3319877127 ISBN 13: 9783319877129
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume comprises a selection of works presented at the Numerical and Evolutionary Optimization (NEO 2016) workshop held in September 2016 in Tlalnepantla, Mexico. The development of powerful search and optimization techniques is of great importance in today's world and requires researchers and practitioners to tackle a growing number of challenging real-world problems. In particular, there are two well-established and widely known fields that are commonly applied in this area: (i) traditional numerical optimization techniques and (ii) comparatively recent bio-inspired heuristics. Both paradigms have their unique strengths and weaknesses, allowing them to solve some challenging problems while still failing in others.The goal of the NEO workshop series is to bring together experts from these and related fields to discuss, compare and merge their complementary perspectives in order to develop fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of the underlying paradigms. In doing so, NEO promotes the development of new techniques that are applicable to a broader class of problems. Moreover, NEO fosters the understanding and adequate treatment of real-world problems particularly in emerging fields that affect all of us, such as healthcare, smart cities, big data, among many others. The extended papers presented in the book contribute to achieving this goal.