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Conference EMO (Conference) (10th : 2019 : East Lansing, Mich.)
Title Evolutionary multi-criterion optimization : 10th international conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019 proceedings / Kalyanmoy Deb [and 6 more], editors.
Imprint Cham, Switzerland : Springer, 2019.

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Conference EMO (Conference) (10th : 2019 : East Lansing, Mich.)
Series Lecture notes in computer science ; 11411.
LNCS sublibrary. SL 1, Theoretical computer science and general issues.
Lecture notes in computer science ; 11411.
LNCS sublibrary. SL 1, Theoretical computer science and general issues.
Subject Mathematical optimization -- Congresses.
Evolutionary computation -- Congresses.
Alt Name Deb, Kalyanmoy,
Add Title EMO 2019
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Conference EMO (Conference) (10th : 2019 : East Lansing, Mich.)
Series Lecture notes in computer science ; 11411.
LNCS sublibrary. SL 1, Theoretical computer science and general issues.
Lecture notes in computer science ; 11411.
LNCS sublibrary. SL 1, Theoretical computer science and general issues.
Subject Mathematical optimization -- Congresses.
Evolutionary computation -- Congresses.
Alt Name Deb, Kalyanmoy,
Add Title EMO 2019
Description 1 online resource.
Note International conference proceedings.
Includes author index.
Online resource; title from PDF title page (SpringerLink, viewed March 7, 2019).
Summary This book constitutes the refereed proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019 held in East Lansing, MI, USA, in March 2019. The 59 revised full papers were carefully reviewed and selected from 76 submissions. The papers are divided into 8 categories, each representing a key area of current interest in the EMO field today. They include theoretical developments, algorithmic developments, issues in many-objective optimization, performance metrics, knowledge extraction and surrogate-based EMO, multi-objective combinatorial problem solving, MCDM and interactive EMO methods, and applications.
Contents Intro; Preface; Organization; Contents; Theory; On Bi-objective Convex-Quadratic Problems; 1 Introduction; 2 Theoretical Properties of Bi-objective Convex-Quadratic Problems; 2.1 Preliminaries; 2.2 Pareto Set; 2.3 Convexity of the Pareto Front; 3 New Classes of Bi-objective Test Functions; 4 Summary; References; An Empirical Investigation of the Optimality and Monotonicity Properties of Multiobjective Archiving Methods; 1 Introduction; 2 Experimental Design; 2.1 Assessment Indexes; 2.2 Archivers Investigated; 2.3 Test Problems; 2.4 General Experimental Settings; 3 Results; 3.1 Optimal Ratio
3.2 Deterioration Ratio3.3 Summary; 4 Concluding Remarks; References; Evolutionary Multi-objective Optimization Using Benson's Karush-Kuhn-Tucker Proximity Measure; 1 Introduction; 2 KKT Based Proximity Measure; 3 Proposed B-KKT Proximity Measure; 4 Results; 4.1 Two-Objective Optimization Problems; 4.2 Three-Objective Optimization Problems; 4.3 Many-Objective Optimization Problems; 4.4 Engineering Design Problem; 5 Conclusions; References; On the Convergence of Decomposition Algorithms in Many-Objective Problems; 1 Introduction; 2 Numerical Experiments; 3 Interpretation of Results
4 ConclusionReferences; Algorithms; A New Hybrid Metaheuristic for Equality Constrained Bi-objective Optimization Problems; 1 Introduction; 2 Background; 3 Proposed Algorithm (M-NSGA-II/PT); 3.1 First Stage: Rough Approximation via Micro-NSGA-II; 3.2 Second Stage: Refinement via PT; 4 Numerical Results; 5 Conclusions and Future Work; References; Make Evolutionary Multiobjective Algorithms Scale Better with Advanced Data Structures: Van Emde Boas Tree for Non-dominated Sorting; 1 Introduction; 2 Preliminaries; 3 The Divide-and-Conquer Algorithm for Non-dominated Sorting; 3.1 The General Plan
3.2 Sweep Line Algorithms for m = 24 The Van Emde Boas Tree; 5 Efficient Implementation of the Van Emde Boas Tree; 6 Implementation and Analysis of the Whole Algorithm; 7 Experiments; 8 Conclusion; References; Toward a New Family of Hybrid Evolutionary Algorithms; 1 Introduction; 2 Background; 3 Subspace Polynomial Mutation Operator; 4 Multi-objective Descent Directions Within MOEAs; 4.1 Equality Constrained MOPs; 4.2 Gradient-Free Descent Direction; 5 Application: Hybrid Algorithm for Constrained Optimization; 6 Conclusions and Future Work; References
Adjustment of Weight Vectors of Penalty-Based Boundary Intersection Method in MOEA/D1 Introduction; 2 Related Works; 3 MOEA/D-PBI with Adjusted Weight Vectors; 4 Computational Experiments; 4.1 Experimental Settings; 4.2 Experimental Results; 5 Conclusions; References; GDE4: The Generalized Differential Evolution with Ordered Mutation; 1 Introduction; 2 Background Review; 2.1 Generalized Differential Evolution; 2.2 Existing Single Objective Differential Evolution with Ordered Mutation; 3 Proposed Algorithm: The Generalized Differential Evolution with the Ordered Mutation (GDE4); 4 Experiment
ISBN 9783030125981 (electronic bk.)
303012598X (electronic bk.)
9783030125974 (print)
ISBN/ISSN 10.1007/978-3-030-12598-1
OCLC # 1089342538
Additional Format Printed edition: 9783030125974.
Printed edition: 9783030125998.


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