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Recent Advances in Evolutionary Multi-objective Optimization: Adaptation, Learning, and Optimization, cartea 20

Editat de Slim Bechikh, Rituparna Datta, Abhishek Gupta
en Limba Engleză Paperback – 14 iun 2018
This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.
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Specificații

ISBN-13: 9783319827094
ISBN-10: 331982709X
Ilustrații: XII, 179 p. 42 illus., 27 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.28 kg
Ediția:Softcover reprint of the original 1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Adaptation, Learning, and Optimization

Locul publicării:Cham, Switzerland

Cuprins

Multi-objective Optimization: Classical
and Evolutionary Approaches.- Dynamic Multi-objective Optimization using Evolutionary
Algorithms: A Survey.- Evolutionary Bilevel Optimization: An Introduction
and Recent Advances.- Many-objective Optimization using Evolutionary Algorithms:
A Survey.- On the Emerging Notion of Evolutionary
Multitasking: A Computational Analog of Cognitive
Multitasking.- Practical Applications in Constrained Evolutionary
Multi-objective Optimization.

Textul de pe ultima copertă

This book covers the most recent advances in the field of evolutionary multiobjective
optimization. With the aim of drawing the attention of up-andcoming
scientists towards exciting prospects at the forefront of computational
intelligence, the authors have made an effort to ensure that the ideas conveyed
herein are accessible to the widest audience. The book begins with a summary
of the basic concepts in multi-objective optimization. This is followed by brief
discussions on various algorithms that have been proposed over the years for
solving such problems, ranging from classical (mathematical) approaches to
sophisticated evolutionary ones that are capable of seamlessly tackling practical
challenges such as non-convexity, multi-modality, the presence of multiple
constraints, etc. Thereafter, some of the key emerging aspects that are likely
to shape future research directions in the field are presented. These include:<
optimization in dynamic environments, multi-objective bilevel programming,
handling high dimensionality under many objectives, and evolutionary multitasking.
In addition to theory and methodology, this book describes several
real-world applications from various domains, which will expose the readers
to the versatility of evolutionary multi-objective optimization.

Caracteristici

Provides both methodological treatments and real world insights Serves as comprehensive reference for researchers, practitioners, and advanced-level students Covers both the theory and practice of using evolutionary algorithms in tackling real world applications involving multiple objectives Includes supplementary material: sn.pub/extras