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

Por: Talbi, El-Ghazali [editor.]Tipo de material: TextoTextoSeries Studies in Computational Intelligence, 434Editor: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Descripción: XXVI, 458 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642306716Trabajos contenidos: SpringerLink (Online service)Tema(s): Engineering | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Formatos físicos adicionales: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q342Recursos en línea: de clik aquí para ver el libro electrónico
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Springer eBooksResumen: 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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