Hybrid Metaheuristics

Inbunden, Engelska, 2012

Del 434 i serien Studies in Computational Intelligence

2 221 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

The main goal of this book is to provide a state of the art of hybrid metaheuristics. The book provides a complete background that enables readers to design and implement hybrid metaheuristics to solve complex optimization problems (continuous/discrete, mono-objective/multi-objective, optimization under uncertainty) in a diverse range of application domains. Readers learn to solve large scale problems quickly and efficiently combining metaheuristics with complementary metaheuristics, mathematical programming, constraint programming and machine learning. Numerous real-world examples of problems and solutions demonstrate how hybrid metaheuristics are applied in such fields as networks, logistics and transportation, bio-medical, engineering design, scheduling.

Produktinformation

Utgivningsdatum:2012-08-01
Mått:155 x 235 x 31 mm
Vikt:887 g
Format:Inbunden
Språk:Engelska
Serie:Studies in Computational Intelligence
Antal sidor:458
Upplaga:2013
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783642306709

Utforska kategorier

Artificiell intelligens inom Data och IT

Recensioner i media

From the book reviews: "Hybrid Metaheuristics is an excellent manuscript for a reader who wants to understand state-of-the-art hybrid metaheuristics and their applications. ... this book could be a source of inspiration for those looking for new procedures for hybridizing metaheuristics and new application areas for metaheuristic techniques. ... a good reference for researchers, practitioners, and students of operations research or computer science who want to have a complete view of metaheuristics and the process of obtaining new procedures by hybridization." (Javier Faulin, Interfaces, Vol. 44 (5), September-October, 2014)

Innehållsförteckning

Part I Hybrid metaheuristics for mono and multi-objective optimization, and optimization under uncertainty.- Part II Combining metaheuristics with (complementary) metaheuristics.- Part III Combining metaheuristics with exact methods from mathematical programming approaches.- Part IV Combining metaheuristics with constraint programming approaches.- Part V Combining metaheuristics with machine learning and data mining techniques.