عنوان
پدید آورنده
موضوع
رده
QA402.5 .M85 2018
QA402
.
5
.
M85
2018
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
9789811314711
(Number (ISBN
9789811314728
(Number (ISBN
9789811346392
(Number (ISBN
9811314713
(Number (ISBN
9811314721
(Number (ISBN
9811346399
Erroneous ISBN
9789811314704
Erroneous ISBN
9811314705
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Multi-objective optimization :
General Material Designation
[Book]
Other Title Information
evolutionary to hybrid framework /
First Statement of Responsibility
Jyotsna K. Mandal, Somnath Mukhopadhyay, Paramartha Dutta, editors.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Singapore :
Name of Publisher, Distributor, etc.
Springer,
Date of Publication, Distribution, etc.
[2018]
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (xvi, 318 pages) :
Other Physical Details
illustrations (some color)
CONTENTS NOTE
Text of Note
Intro; Foreword; Editorial Preface; Contents; About the Editors; Non-dominated Sorting Based Multi/Many-Objective Optimization: Two Decades of Research and Application; 1 Introduction; 2 Across Different Scenarios; 2.1 Multi/Many-Objective Optimization; 2.2 Single-objective Optimization; 3 Recent Non-dominated Sorting Based Algorithms; 3.2 Other Successful Algorithms; 4 State-of-the-Art Combinations; 4.1 Alternating Phases; 4.2 Two Local Search Operators; 4.3 B-NSGA-III Results; 5 Conclusions; References; Mean-Entropy Model of Uncertain Portfolio Selection Problem.
Text of Note
1 Introduction2 Literature Study; 3 Preliminaries; 4 Uncertain Multi-Objective Programming; 4.1 Weighted Sum Method; 4.2 Weighted Metric Method; 5 Multi-Objective Genetic Algorithm; 5.1 Nondominated Sorting Genetic Algorithm II (NSGA-II); 5.2 Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D); 6 Performance Metrics; 7 Proposed Uncertain Bi-Objective Portfolio Selection Model; 8 Results and Discussion; 9 Conclusion; References.
Text of Note
3 Definitions of Variables and Parameters4 Descriptions of Goals and Constraints; 4.1 Performance Measure Goals; 4.2 System Constraints; 5 An Illustrative Example; 5.1 Construction of Model Goals; 5.2 Description of Constraints; 5.3 Performance Comparison; 6 Conclusions and Future Scope; References; Multi-objective Optimization to Improve Robustness in Networks; 1 Introduction; 1.1 Robustness Measures Based on the Eigenvalues of the Adjacency Matrix; 1.2 Measures Based on the Eigenvalues of the Laplacian Matrix; 1.3 Measures Based on Other Properties.
Text of Note
4.2 Computation of Fitness Functions4.3 Genetic Operators; 4.4 Final Solution from the Non-dominated Front; 5 Experimental Results and Discussion; 5.1 Dataset and Preprocessing; 5.2 Experimental Setup; 5.3 Study of GO Enrichment; 5.4 Study of KEGG Pathway Enrichment; 6 Conclusion; References; Interval-Valued Goal Programming Method to Solve Patrol Manpower Planning Problem for Road Traffic Management Using Genetic Algorithm; 1 Introduction; 2 IVGP Formulation; 2.1 Deterministic Flexible Goals; 2.2 IVGP Model; 2.3 The IVGP Algorithm; 2.4 GA Computational Scheme for IVGP Model.
Text of Note
Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data1 Introduction; 2 Gene Ontology and Similarity Measures; 2.1 Resnik's Measure; 2.2 Lin's Measure; 2.3 Weighted Jaccard Measure; 2.4 Combining Expression-Based and GO-Based Distances; 3 Multiobjective Optimization and Clustering; 3.1 Formal Definitions; 3.2 Multiobjective Clustering; 4 Incorporating GO Knowledge in Multiobjective Clustering; 4.1 Chromosome Representation and Initialization of Population.
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8
8
8
8
SUMMARY OR ABSTRACT
Text of Note
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.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Springer Nature
Stock Number
com.springer.onix.9789811314711
OTHER EDITION IN ANOTHER MEDIUM
Title
Multi-objective optimization.
International Standard Book Number
9789811314704
TOPICAL NAME USED AS SUBJECT
Mathematical optimization.
Mathematical optimization.
MATHEMATICS-- Applied.
MATHEMATICS-- Probability & Statistics-- General.
(SUBJECT CATEGORY (Provisional
MAT-- 003000
MAT-- 029000
UYA
UYA
UYAM
UYAM
DEWEY DECIMAL CLASSIFICATION
Number
519
.
6
Edition
23
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA402
.
5
Book number
.
M85
2018
PERSONAL NAME - ALTERNATIVE RESPONSIBILITY
Dutta, Paramartha
Mandal, Jyotsna Kumar,1960-
Mukhopadhyay, Somnath,1983-
ORIGINATING SOURCE
Date of Transaction
20200823224912.0
Cataloguing Rules (Descriptive Conventions))
pn
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب [Book]
Y
