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03509nam a22004815i 4500 |
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|a 9783319429786
|9 978-3-319-42978-6
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|a 10.1007/978-3-319-42978-6
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|a Recent Advances in Evolutionary Multi-objective Optimization
|h [electronic resource] /
|c edited by Slim Bechikh, Rituparna Datta, Abhishek Gupta.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a XII, 179 p. 42 illus., 27 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
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|a online resource
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|a text file
|b PDF
|2 rda
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|a Adaptation, Learning, and Optimization,
|x 1867-4534 ;
|v 20
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| 505 |
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|a 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.
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| 520 |
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|a 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.
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| 650 |
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|a Engineering.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Computational intelligence.
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| 650 |
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4 |
|a Engineering.
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| 650 |
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|a Computational Intelligence.
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| 650 |
2 |
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|a Artificial Intelligence (incl. Robotics).
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| 700 |
1 |
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|a Bechikh, Slim.
|e editor.
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| 700 |
1 |
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|a Datta, Rituparna.
|e editor.
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| 700 |
1 |
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|a Gupta, Abhishek.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9783319429779
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| 830 |
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|a Adaptation, Learning, and Optimization,
|x 1867-4534 ;
|v 20
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1007/978-3-319-42978-6
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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