Many-Criteria Optimization and Decision Analysis / Natural Computing Series (PDF)
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This book presents the state-of-the-art, current challenges, and future perspectives for the field of many-criteria optimization and decision analysis. The field recognizes that real-life problems often involve trying to balance a multiplicity of considerations simultaneously - such as performance, cost, risk, sustainability, and quality. The field develops theory, methods and tools that can support decision makers in finding appropriate solutions when faced with many (typically more than three) such criteria at the same time.
The book consists of two parts: key research topics, and emerging topics. Part I begins with a general introduction to many-criteria optimization, perspectives from research leaders in real-world problems, and a contemporary survey of the attributes of problems of this kind. This part continues with chapters on fundamental aspects of many-criteria optimization, namely on order relations, quality measures, benchmarking, visualization, and theoretical considerations. Part II offers more specialized chapters on correlated objectives, heterogeneous objectives, Bayesian optimization, and game theory.
Written by leading experts across the field of many-criteria optimization, this book will be an essential resource for researchers in the fields of evolutionary computing, operations research, multiobjective optimization, and decision science.
Michael Emmerich is an Associate Professor at Leiden University and has recently also been an international research fellow at the University of Jyväskylä, Finland. At Leiden University he leads since 2011 the Multi-objective Optimization and Decision Analysis (MODA) research group. He received his doctorate in 2005 on the topic "Gaussian Process Models in Multiobjective Optimization" from TU Dortmund, Germany (promotor: Hans-Paul Schwefel). He chaired four leading international optimization conferences and five Lorentz center workshops. He published more than 200 articles on multiobjective optimization, mainly indicator-based algorithms and Bayesian multiobjective optimization, and applications such as architectural design, logistics, and computational chemistry.
Boris Naujoks is a professor of Applied Mathematics at TH Köln - Cologne University of Applied Sciences (THK). He joined THK directly after he received his PhD from Dortmund Technical University in 2011. During his time in Dortmund, Boris worked as a research assistant in different projects and gained industrial experience working for different SMEs. Now, he enjoys the combination of teaching mathematics as well as computer science and exploring EC and CI techniques at the Campus Gummersbach of THK. He focuses on multiobjective (evolutionary) optimization, in particular hypervolume based algorithms, benchmarking, and the (industrial) applicability of such techniques.
Robin Purshouse is Professor of Decision Sciences at the University of Sheffield, UK. He received his PhD in
- 2023, 1st ed. 2023, 360 Seiten, Englisch
- Herausgegeben: Dimo Brockhoff, Michael Emmerich, Boris Naujoks, Robin Purshouse
- Verlag: Springer International Publishing
- ISBN-10: 3031252632
- ISBN-13: 9783031252631
- Erscheinungsdatum: 28.07.2023
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Grösse: 15 MB
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