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Hybrid Metaheuristics [electronic resource] / edited by El-Ghazali Talbi.

Contributor(s): Talbi, El-Ghazali [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 434Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XXVI, 458 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642306716.Subject(s): Engineering | Artificial intelligence | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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