Overview
- 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
Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 20)
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Table of contents (6 chapters)
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Bibliographic Information
Book Title: Recent Advances in Evolutionary Multi-objective Optimization
Editors: Slim Bechikh, Rituparna Datta, Abhishek Gupta
Series Title: Adaptation, Learning, and Optimization
DOI: https://doi.org/10.1007/978-3-319-42978-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2017
Hardcover ISBN: 978-3-319-42977-9Published: 18 August 2016
Softcover ISBN: 978-3-319-82709-4Published: 14 June 2018
eBook ISBN: 978-3-319-42978-6Published: 09 August 2016
Series ISSN: 1867-4534
Series E-ISSN: 1867-4542
Edition Number: 1
Number of Pages: XII, 179
Number of Illustrations: 15 b/w illustrations, 27 illustrations in colour