Multi-Objective Optimization Evolutionary to Hybrid Framework /

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 variou...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Mandal, Jyotsna K. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Mukhopadhyay, Somnath (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Dutta, Paramartha (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2018.
Έκδοση:1st ed. 2018.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 04606nam a2200517 4500
001 978-981-13-1471-1
003 DE-He213
005 20191021171732.0
007 cr nn 008mamaa
008 180818s2018 si | s |||| 0|eng d
020 |a 9789811314711  |9 978-981-13-1471-1 
024 7 |a 10.1007/978-981-13-1471-1  |2 doi 
040 |d GrThAP 
050 4 |a QA76.9.M35 
072 7 |a UYA  |2 bicssc 
072 7 |a COM018000  |2 bisacsh 
072 7 |a UYA  |2 thema 
072 7 |a UYAM  |2 thema 
082 0 4 |a 004.0151  |2 23 
245 1 0 |a Multi-Objective Optimization  |h [electronic resource] :  |b Evolutionary to Hybrid Framework /  |c edited by Jyotsna K. Mandal, Somnath Mukhopadhyay, Paramartha Dutta. 
250 |a 1st ed. 2018. 
264 1 |a Singapore :  |b Springer Singapore :  |b Imprint: Springer,  |c 2018. 
300 |a XVI, 318 p. 90 illus., 51 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Chapter 1. An Advance Overview of Single and Multi-Objective Optimization -- Chapter 2. Non-dominated Sorting Based Multi/Many Objective Optimization: Two Decades of Research and Application -- Chapter 3. Uncertain Multi-objective Portfolio Selection Model based on Genetic Algorithm -- Chapter 4. A Multiobjective Genetic Algorithm-based Approach for Identifying Relevant and Non-redundant Cancer-MicroRNA Markers -- Chapter 5. Application of Multi-objective Optimizations in Protein Structure Prediction -- Chapter 6. Multi-target Multiobjective Programming and Patrol Manpower Planning for Traffic Management via Genetic Algorithm -- Chapter 7. Multi-objective Optimization for Key Player Identification in Networks -- Chapter 8. Joint Maximization in Energy and Spectral Efficiency in Cooperative Cognitive Radio Networks -- Chapter 9. A Neoteric Multi-Objective Framework for Engineering Process Optimization: Metaheuristics and Experimental Designs based Approach -- Chapter 10. Multi/Many Objective Optimization - Hybrid Intelligent Framework -- Chapter 11. Efficiency Maximization of Multimedia Data Mining using Multiobjective Neuro-ACO Approach -- Chapter 12. Optimized Determination of Separating Hyper-Plane of an SVM - Hybrid Multiobjective Model -- Chapter 13. Efficient Cluster Head Selection in Wireless Sensor Network using Multiobjective Model -- Chapter 14. Achieving Optimized Bio-Metric Security in E-Governance by Multiobjective Neuro Approach -- Chapter 15. Advantage of Quantum Inspired Multiobjective Genetic Algorithm over Classical Multiobjective Genetic Algorithm -- Chapter 16. Optimizing Performance Parameter of Image Segmentation using Hybrid Multiobjective Framework. 
520 |a 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. 
650 0 |a Computer science-Mathematics. 
650 0 |a Mathematical optimization. 
650 0 |a Computational intelligence. 
650 1 4 |a Mathematics of Computing.  |0 http://scigraph.springernature.com/things/product-market-codes/I17001 
650 2 4 |a Optimization.  |0 http://scigraph.springernature.com/things/product-market-codes/M26008 
650 2 4 |a Computational Intelligence.  |0 http://scigraph.springernature.com/things/product-market-codes/T11014 
700 1 |a Mandal, Jyotsna K.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Mukhopadhyay, Somnath.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Dutta, Paramartha.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9789811314704 
776 0 8 |i Printed edition:  |z 9789811314728 
776 0 8 |i Printed edition:  |z 9789811346392 
856 4 0 |u https://doi.org/10.1007/978-981-13-1471-1  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)