Multi-Objective Optimization Evolutionary to Hybrid Framework /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Mandal, Jyotsna K. (Editor, http://id.loc.gov/vocabulary/relators/edt), Mukhopadhyay, Somnath (Editor, http://id.loc.gov/vocabulary/relators/edt), Dutta, Paramartha (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Singapore : Springer Singapore : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Subjects: Computer science-Mathematics.; Mathematical optimization.; Computational intelligence.; Mathematics of Computing.; Optimization.; Computational Intelligence.
• Physical Description: XVI, 318 p. 90 illus., 51 illus. in color. online resource.
• ISBN: 9789811314711
• DOI: 10.1007/978-981-13-1471-1

This book brings together the latest findings on efficient solutions of multi/many-objective optimization problems from the leading researchers in the field. The focus is on solving real-world optimization problems using strategies ranging from evolutionary to hybrid frameworks, and involving various computation platforms. The topics covered include solution frameworks using evolutionary to hybrid models in application areas like Analytics, Cancer Research, Traffic Management, Networks and Communications, E-Governance, Quantum Technology, Image Processing, etc. As such, the book offers a valuable resource for all postgraduate students and researchers interested in exploring solution frameworks for multi/many-objective optimization problems.