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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
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
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
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
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
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
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
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
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