Learning and Intelligent Optimization, 13 conf., LION 13

Preface......Page 6
Organization......Page 8
Contents......Page 11
 1 Introduction......Page 14
 2 The Weighted Feedback Vertex Set Problem......Page 15
 3 Hybrid-IA: A Hybrid Immunological Algorithm......Page 16
 4 Results......Page 20
  4.1 Dynamic Behaviour......Page 21
  4.2 Experiments and Comparisons......Page 24
 5 Conclusion......Page 27
 References......Page 28
 1 Introduction......Page 30
 2 Related Work......Page 32
  3.1 Underlying Assumptions and Densities......Page 33
  3.2 (Sub)Graph Densities and the  Statistic......Page 35
  3.3 Statistical Hypotheses......Page 36
  3.5 Methodological Comparison to Other Recently Published Techniques......Page 37
  3.7 Empirical Results......Page 38
 4 Conclusion......Page 40
 References......Page 41
 1 Introduction......Page 43
 2 Related Work......Page 44
 3 Binary Decision Diagrams (BDDs)......Page 45
 4 State Similarity......Page 49
 5 Computational Study......Page 53
 6 Conclusion and Future Work......Page 56
 References......Page 57
 2 Problem Formulation, Its Sources and Related Problems......Page 59
 3 Some Auxiliary Statements: Properties of the Problem1 Optimal Solution in the 1D Case......Page 61
 4 Polynomial Solvability of the Problem in the 1D Case......Page 62
 References......Page 64
 1 Introduction......Page 66
 2 Preliminaries......Page 67
  3.1 Encoding CA Problems as Set Cover Problems......Page 68
  3.2 Boltzmann Machines for Set Cover Problems......Page 69
  3.3 Computing CAs with Boltzmann Machines......Page 70
 4 Finding Covering Arrays with Neural Networks......Page 72
  4.1 Weight Updates: A First Step Towards Learning......Page 74
  4.2 Graph Updates: An Additional Layer for Learning......Page 76
 5 Experimental Evaluation......Page 77
  5.1 Tested Configurations of Algorithm2......Page 78
  5.2 Tested Configurations of Algorithm3......Page 79
 6 Conclusion......Page 80
 References......Page 81
 1 Introduction......Page 82
 2 Adaptive Sequence-Based Heuristic......Page 83
 3 Computational Results......Page 85
 References......Page 88
 1 Introduction......Page 90
  2.2 Phase 1 – Feasibility Determination......Page 92
  2.3 Phase 2 – Bayesian Optimization in the Estimated Feasible Region......Page 94
  3.1 Test Functions......Page 95
  3.2 Pump Scheduling Optimization Case Study......Page 96
  3.3 SVM-CBO Settings......Page 99
  4.1 Results on€Test Functions......Page 101
  4.2 Results on€the€PSO Case Study......Page 102
 5 Conclusions......Page 103
 References......Page 104
 1 Introduction......Page 107
 2 The HF-CSAM Algorithm......Page 109
 3 Experimental Results......Page 113
  3.1 Results on the MNIST Dataset......Page 114
  3.2 Results on the CIFAR10 Dataset......Page 115
 4 Conclusions......Page 117
 References......Page 118
 1 Introduction......Page 119
 2 Routing Open Shop: Formal Description and Notation......Page 122
 3 Irreducible Bin Packing......Page 124
 4 The Main Result: The Connection Between IBP(2) and RO2||Rmax......Page 127
 5 Concluding Remarks......Page 131
 References......Page 132
 1 Introduction......Page 134
 2 Stochastic Surgery Scheduling......Page 136
 3 SV-ORDS Classifier......Page 139
  4.1 Instance Generation......Page 140
  4.2 Results......Page 141
 5 Discussion and Conclusion......Page 145
 References......Page 146
 1 Introduction......Page 148
 2 Problem Formulation, Related Problem, and Complexity......Page 149
 3 Foundations of the Algorithm......Page 151
 4 Exact Algorithm......Page 154
 5 Examples of Numerical Simulation......Page 155
 6 Conclusion......Page 157
 References......Page 158
 1 Introduction......Page 159
  2.1 A Literature Review......Page 160
  2.2 Mathematical Model......Page 161
  3.1 Stages of the Methodology......Page 162
  3.4 Improvisation Stage......Page 163
 4 Analysis of the Experimental Results......Page 164
 5 Conclusion......Page 166
 References......Page 169
 1 Introduction......Page 170
 2 Problem Statement......Page 171
  3.2 Neighborhood Structures......Page 174
  3.4 Adaptive General Variable Neighborhood Search......Page 175
  4.2 Numerical Analysis......Page 179
 References......Page 182
 1 Introduction......Page 184
 2 Single Source Capacitated Facility Location Problem......Page 185
 3 Related Work......Page 186
 4 RAMP Algorithm for€the€SSCFLP......Page 188
  4.2 Projection Method......Page 189
  4.3 Primal Phase......Page 190
 5 Computational Results......Page 191
 References......Page 194
A Novel Approach for Solving Large-Scale Bike Sharing Station Planning Problems......Page 197
 1 Introduction......Page 198
 2 Related Work......Page 199
 3 Problem Definition......Page 201
  3.2 Optimization Goal......Page 204
 4 Multilevel Refinement Approach......Page 205
  4.1 Coarsening......Page 206
  4.2 Initialization......Page 207
  4.4 Refinement......Page 209
 5 Computational Results......Page 210
 6 Conclusion......Page 211
 References......Page 212
 1 Introduction......Page 214
 2 Greedy Approximation Algorithms......Page 215
  2.1 Analysis of the Algorithms on Random Inputs......Page 216
 3 Conclusion......Page 219
 References......Page 220
 1 Introduction......Page 221
 2 Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem......Page 223
 3 Parallel Multi-Start Multiobjective Artificial Bee Colony Algorithm (PMS-ABC)......Page 225
 4 Computational Results......Page 227
 5 Conclusions and Future Research......Page 233
 References......Page 235
 1 Introduction......Page 237
 2 Related Work......Page 238
 4 Approximation Scheme......Page 239
  4.2 Instance Decomposition......Page 240
 References......Page 241
 1 Introduction......Page 244
 2 Related Work......Page 245
  3.1 Parallel GVNS - Managed Information Exchange Model......Page 246
  3.2 Parallel GVNS - A Model with Isolated Processes......Page 247
  3.3 Model Classification......Page 248
 4 Computational Experiments......Page 249
 5 Conclusions......Page 251
 References......Page 252
 1 Introduction......Page 253
 2 Related Work......Page 254
  3.1 The Dual Method......Page 256
  3.3 Primal Method......Page 258
 4 Computational Results......Page 259
 References......Page 263
 1 Introduction......Page 266
 2 Capacitated Team Orienteering Problem......Page 268
 3 Differential Evolution Algorithm......Page 270
 4 The Proposed Distance Related Solution Encoding/Decoding Scheme......Page 271
 5 Distance Related Differential Evolution Algorithm (DRDE)......Page 273
  6.2 Results......Page 276
 7 Conclusions......Page 278
 References......Page 279
 1 Introduction......Page 281
 2 Literature Review......Page 282
 3 Energy Problems......Page 283
  3.2 Constraints of€the€Problem......Page 284
 4 Sonar Inspired Optimization......Page 286
  4.2 Objective Functions......Page 288
 5 Experimental Results......Page 289
 6 Future Work......Page 293
 Appendix......Page 294
 References......Page 295
 1 Introduction......Page 297
 2 The Optimal Product Line Design Problem......Page 298
 4 The Proposed Approach......Page 299
  4.1 Problem Formulation......Page 300
  4.2 Proposed Hybrid Approach (FAGA) for€the€Product Line Design Problem......Page 301
  4.3 FAGA Configuration......Page 302
  5.2 Robustness Testing......Page 303
  5.3 Results Using Simulated Data......Page 304
 6 Conclusions......Page 308
 References......Page 309
 1 Introduction......Page 311
 2 Related Works......Page 312
 3 Variable Neighborhood Simulated Annealing Algorithm......Page 313
 4 Computational Results......Page 314
 References......Page 315
 1 Introduction......Page 317
 2 Gaussian Process Regression......Page 318
  2.1 Kernel Functions......Page 319
 3 Quality Estimation and Feature Extraction......Page 320
 4 Kernel Function Search......Page 321
  4.1 Related Work......Page 323
  5.1 Datasets and Embeddings......Page 324
  5.3 Results......Page 325
 6 Conclusions......Page 328
 References......Page 329
 1 Introduction......Page 332
  2.1 Data......Page 333
  2.2 Neural Network Model......Page 334
 References......Page 337
 1 Introduction......Page 338
  1.1 Terminology and Notation......Page 340
 2 The SAT Model......Page 341
 3 Symmetry Breaking......Page 345
  3.1 Finding All Automorphisms and Stabilizers......Page 348
 4 Computational Results......Page 349
 References......Page 353
 1 Introduction......Page 355
  1.1 Background......Page 356
  1.2 Our Contribution......Page 357
 2 Problem Formulation......Page 358
  3.1 Properties of the Feasible Solutions......Page 359
  3.2 Dynamic Programming Algorithm......Page 360
  3.3 Complexity of the Algorithm......Page 363
 4 Simulation......Page 364
 5 Conclusion......Page 365
 References......Page 366
 1 Introduction......Page 368
 2 Problem Formulation......Page 370
  3.1 Local Search......Page 371
  3.3 Variable Neighborhood Search......Page 372
  3.4 Genetic Local Search......Page 373
  3.5 Ant Colony Optimization......Page 375
 4 Simulation......Page 376
 5 Conclusion......Page 380
 References......Page 381
 1 Introduction......Page 383
 2 Halton Sequences......Page 384
  2.1 Discrepancy......Page 385
 3 Optimization of Generalized Halton Sequences......Page 386
 4 Differential Evolution......Page 387
 5 DE for Halton Sequence Optimization......Page 389
 6 Experimental Evaluation......Page 390
 7 Conclusions......Page 393
 References......Page 394
 1 Introduction......Page 396
 2 Brief Introduction to Bayesian Optimization......Page 397
  2.1 Gaussian Processes......Page 398
 3 Related Work......Page 399
 4 Dealing with Multi-modal Problems with BO Optimization......Page 400
  4.2 Adaptive BO with Clustering......Page 402
  4.3 BO with Batch Sampling Strategies......Page 403
  5.1 A Framework for Solving Low-Budget Multi-modal Optimization Problems......Page 404
  5.3 Comparison Between the Different BO Variants......Page 406
  5.4 Comparison to State-of-the-Art EAs for Multi-modal Problems......Page 407
 6 Conclusions......Page 408
 References......Page 409
Author Index......Page 411