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Multi-Objective Optimization - Evolutionary to Hybrid Framework
von: Jyotsna K. Mandal, Somnath Mukhopadhyay, Paramartha Dutta
Springer-Verlag, 2018
ISBN: 9789811314711 , 326 Seiten
Format: PDF, Online Lesen
Kopierschutz: Wasserzeichen
Preis: 160,49 EUR
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Foreword
5
Editorial Preface
6
Contents
11
About the Editors
13
Non-dominated Sorting Based Multi/Many-Objective Optimization: Two Decades of Research and Application
15
1 Introduction
15
2 Across Different Scenarios
19
2.1 Multi/Many-Objective Optimization
19
2.2 Single-objective Optimization
21
3 Recent Non-dominated Sorting Based Algorithms
21
3.1 ?0????????????????
21
3.2 Other Successful Algorithms
25
4 State-of-the-Art Combinations
26
4.1 Alternating Phases
28
4.2 Two Local Search Operators
32
4.3 B-NSGA-III Results
34
5 Conclusions
35
References
35
Mean-Entropy Model of Uncertain Portfolio Selection Problem
39
1 Introduction
39
2 Literature Study
41
3 Preliminaries
43
4 Uncertain Multi-Objective Programming
47
4.1 Weighted Sum Method
49
4.2 Weighted Metric Method
50
5 Multi-Objective Genetic Algorithm
51
5.1 Nondominated Sorting Genetic Algorithm II (NSGA-II)
52
5.2 Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D)
53
6 Performance Metrics
56
7 Proposed Uncertain Bi-Objective Portfolio Selection Model
58
8 Results and Discussion
60
9 Conclusion
64
References
65
Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data
69
1 Introduction
69
2 Gene Ontology and Similarity Measures
70
2.1 Resnik's Measure
71
2.2 Lin's Measure
72
2.3 Weighted Jaccard Measure
72
2.4 Combining Expression-Based and GO-Based Distances
73
3 Multiobjective Optimization and Clustering
73
3.1 Formal Definitions
73
3.2 Multiobjective Clustering
75
4 Incorporating GO Knowledge in Multiobjective Clustering
75
4.1 Chromosome Representation and Initialization of Population
75
4.2 Computation of Fitness Functions
76
4.3 Genetic Operators
77
4.4 Final Solution from the Non-dominated Front
77
5 Experimental Results and Discussion
78
5.1 Dataset and Preprocessing
78
5.2 Experimental Setup
78
5.3 Study of GO Enrichment
79
5.4 Study of KEGG Pathway Enrichment
85
6 Conclusion
91
References
91
Interval-Valued Goal Programming Method to Solve Patrol Manpower Planning Problem for Road Traffic Management Using Genetic Algorithm
93
1 Introduction
93
2 IVGP Formulation
97
2.1 Deterministic Flexible Goals
99
2.2 IVGP Model
100
2.3 The IVGP Algorithm
101
2.4 GA Computational Scheme for IVGP Model
103
3 Definitions of Variables and Parameters
105
4 Descriptions of Goals and Constraints
106
4.1 Performance Measure Goals
106
4.2 System Constraints
114
5 An Illustrative Example
114
5.1 Construction of Model Goals
117
5.2 Description of Constraints
120
5.3 Performance Comparison
124
6 Conclusions and Future Scope
125
References
126
Multi-objective Optimization to Improve Robustness in Networks
128
1 Introduction
128
1.1 Robustness Measures Based on the Eigenvalues of the Adjacency Matrix
128
1.2 Measures Based on the Eigenvalues of the Laplacian Matrix
129
1.3 Measures Based on Other Properties
130
2 Properties of Network Robustness Measures
131
2.1 Robustness of Elementary Networks
132
2.2 Correlation of Robustness Measures
133
3 Multi-objective Definition of Robustness
135
3.1 Fast Calculation of Robustness Measures
136
4 Selecting Solutions from Multi-objective Optimization
137
4.1 Ranking Methods
138
4.2 Pruning Methods
139
4.3 Subset Optimality
140
5 Leave-k-out Approach for Multi-objective Optimization
141
6 Experimental Results
142
6.1 Improving Robustness by Edge Addition
142
6.2 Network Robustness After Node Attacks
147
7 Conclusion
147
References
150
On Joint Maximization in Energy and Spectral Efficiency in Cooperative Cognitive Radio Networks
153
1 Introduction
153
1.1 Machine Learning in CR
155
1.2 Scope and Contributions
156
2 System Model
157
2.1 Signal Model
158
3 Problem Formulation and Proposed Solution
161
4 Numerical Results
164
5 Conclusions
167
References
168
Multi-Objective Optimization Approaches in Biological Learning System on Microarray Data
170
1 Introduction
170
2 Fundamental Terms and Preliminaries
171
2.1 Microarray
172
2.2 Statistical Tests
172
2.3 Epigenetic Biomarker
174
2.4 Multi-Objective Optimization
174
2.5 Pareto-Optimal
175
3 Method Hierarchy
175
4 Description of Methods
176
4.1 Integrated Learning Approach to Classify Multi-class Cancer Data
176
4.2 Multi-Objective Optimization Method on Gene Regularity Networks
176
4.3 Multi-Objective Genetic Algorithm in Fuzzy Clustering of Categorical Attributes
178
4.4 Multi-Objective Differential Evolution for Automatic Clustering of Microarray Datasets
181
4.5 Multi-Objective Particle Swarm Optimization to Identify Gene Marker
182
4.6 Multi-Objective Binary Particle Swarm Optimization Algorithm for Cancer Data Feature Selection
183
4.7 Multi-Objective Approach for Identifying Coexpressed Module During HIV Disease Progression
185
4.8 Other Methods
186
5 Discussion
187
6 Conclusion
189
References
189
Application of Multiobjective Optimization Techniques in Biomedical Image Segmentation—A Study
192
1 Introduction
192
2 Multiobjective Optimization
196
3 Application of Multiobjective Optimization in Biomedical Images
197
4 Conclusion
200
References
202
Feature Selection Using Multi-Objective Optimization Technique for Supervised Cancer Classification
206
1 Introduction
206
2 Experimental Datasets
208
3 Objectives
210
4 Proposed Methodology
210
4.1 Multi-Objective Blended Particle Swarm Optimization (MOBPSO)
211
4.2 Other Comparative Methods for the Selection of Genes
216
5 Experimental Results
217
5.1 Classification Results
218
5.2 Comparative Analysis
219
5.3 Biological Relevance
222
6 Conclusion
223
References
223
Extended Nondominated Sorting Genetic Algorithm (ENSGA-II) for Multi-Objective Optimization Problem in Interval Environment
225
1 Introduction
225
2 Interval Mathematics and Order Relations Between Intervals
227
2.1 Interval Mathematics
227
2.2 Order Relations of Interval Numbers
230
3 Multi-Objective Optimization Problem with Interval Objectives
235
4 Nondominated Sorting Genetic Algorithm for Interval Objectives
235
4.1 Constraint Handling Techniques
236
4.2 Nondominated Sorting
236
4.3 Interval Crowding Distance
237
4.4 Crowded Tournament Selection
239
4.5 Crossover
240
4.6 Mutation
240
4.7 Algorithm
241
5 Numerical Simulation
242
6 Concluding Remarks
248
Appendix
248
References
251
A Comparative Study on Different Versions of Multi-Objective Genetic Algorithm for Simultaneous Gene Selection and Sample Categorization
252
1 Introduction
252
2 Brief Overview of State-of-the-Art Methods
253
3 Proposed Methodology
256
3.1 Initial Population and External Population
257
3.2 Fitness Function
259
3.3 Tournament Selection
263
3.4 Crossover Operation
263
3.5 Mutation Operation
264
3.6 Multi-Objective Genetic Algorithm for Gene Selection and Sample Clustering
264
4 Experimental Results
271
4.1 Microarray Dataset Description
271
4.2 Parameter Setup and Preprocessing
272
4.3 Performance Measurement
272
5 Summary
274
References
274
A Survey on the Application of Multi-Objective Optimization Methods in Image Segmentation
277
1 Introduction
277
2 Image Segmentation and MOO
278
3 Image Segmentation Design Issue
279
4 Image Segmentation Classification Using Multi-Objective Perspective
280
5 Survey on Image Application Including MOO
282
6 Conclusion
284
References
284
Bi-objective Genetic Algorithm with Rough Set Theory for Important Gene Selection in Disease Diagnosis
287
1 Introduction
287
2 Bi-objective Gene Selection
289
2.1 Initial Population Generation
289
2.2 Bi-objective Objective Function
290
2.3 Multipoint Crossover
294
2.4 Jumping Gene Mutation
294
2.5 Replacement Strategy
295
2.6 The GSBOGA Algorithm
296
3 Experimental Results of GSBOGA Method
298
3.1 Microarray Dataset Description
298
3.2 Parameter Setup and Preprocessing
299
3.3 Performance Measurement
299
3.4 Comparative Study
301
4 Summary
304
References
304
Multi-Objective Optimization and Cluster-Wise Regression Analysis to Establish Input–Output Relationships of a Process
307
1 Introduction
307
2 Literature Survey
309
3 Developed Approach
310
4 Experimental Data Collection
312
4.1 Experimental Setup and Procedure
313
4.2 Data Collection
313
5 Results and Discussion
314
5.1 Obtaining Nonlinear Input–Output Relationships from the Experimental Data
314
5.2 Formulation of the Optimization Problem
315
5.3 Obtaining Initial Pareto-Front
315
5.4 Training of an NFS
317
5.5 Obtaining Modified Pareto-Front
318
5.6 Clustering of the Modified Pareto-Front Data Set
318
6 Conclusion
322
Appendices
323
References
324