Evolutionary computation for dynamic optimization problems:
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| Format: | Book |
| Language: | English |
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Berlin [u.a.]
Springer
2013
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| Series: | Studies in computational intelligence
490 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XXVIII, 470 S. Ill., graph. Darst. |
| ISBN: | 9783642384158 9783642384165 |
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| 245 | 1 | 0 | |a Evolutionary computation for dynamic optimization problems |c Shengxiang Yang ... ed. |
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IMAGE 1
CONTENTS
PART I: FUNDAMENTALS 1 EVOLUTIONARY DYNAMIC OPTIMIZATION: TEST AND
EVALUATION ENVIRONMENTS 3
SHENGXIANG YANG, TRUNG THANH NGUYEN, CHANGHE LI 1.1 INTRODUCTION 3
1.2 DOPS: CONCEPTS, BRIEF REVIEW, AND CLASSIFICATION 5
1.2.1 CONCEPTS O F DOPS 5
1.2.2 DYNAMIC TEST PROBLEMS: BRIEF REVIEW 5
1.2.3 MAJOR CHARACTERISTICS AND CLASSIFICATION O F DOPS 6
1.3 TYPICAL DYNAMIC TEST PROBLEMS AND GENERATORS 8
1.3.1 DYNAMIC TEST PROBLEMS IN THE REAL SPACE 8
1.3.2 DYNAMIC TEST PROBLEMS IN THE BINARY SPACE 10
1.3.3 DYNAMIC TEST PROBLEMS IN THE COMBINATORIAL S P A C E . . . . 13
1.4 PERFORMANCE METRICS 16
1.4.1 OPTIMALITY-BASED PERFORMANCE MEASURES 16
1.4.2 BEHAVIOUR-BASED PERFORMANCE MEASURES 21
1.4.3 DISCUSSION 24
1.5 THE GENERALIZED DYNAMIC BENCHMARK GENERATOR (GDBG) 25
1.5.1 DYNAMIC ROTATION PEAK BENCHMARK GENERATOR 27
1.5.2 DYNAMIC COMPOSITION BENCHMARK GENERATOR 28
1.5.3 DYNAMIC TEST PROBLEMS FOR THE CEC 2009 COMPETITION 29
1.6 CONCLUSIONS AND DISCUSSIONS 31
REFERENCES 32
2 EVOLUTIONARY DYNAMIC OPTIMIZATION: METHODOLOGIES 39
TRUNG THANH NGUYEN, SHENGXIANG YANG, JUERGEN BRANKE, XIN YAO 2.1
INTRODUCTION 39
2.2 OPTIMIZATION APPROACHES 4 0
HTTP://D-NB.INFO/1033706876
IMAGE 2
X V I
CONTENTS
2.2.1 THE GOALS O F EDO ALGORITHMS 4 0
2.2.2 DETECTING CHANGES 41
2.2.3 INTRODUCING DIVERSITY WHEN CHANGES OCCUR 4 2
2.2.4 MAINTAINING DIVERSITY DURING THE SEARCH 44
2.2.5 MEMORY APPROACHES 4 6
2.2.6 PREDICTION APPROACHES 48
2.2.7 SELF-ADAPTIVE APPROACHES 5 0
2.2.8 MULTI-POPULATION APPROACHES 51
2.3 ' THEORETICAL DEVELOPMENT O F EDO METHODOLOGIES 53
2.4 SUMMARY AND FUTURE RESEARCH DIRECTIONS 55
2.4.1 SUMMARY 55
2.4.2 THE GAPS BETWEEN ACADEMIC RESEARCH AND REAL-WORLD PROBLEMS 55
2.4.3 FUTURE RESEARCH DIRECTIONS 56
REFERENCES 57
3 EVOLUTIONARY DYNAMIC OPTIMIZATION: CHALLENGES AND PERSPECTIVES 65
PHILIPP ROHLFSHAGEN, XIN YAO 3.1 INTRODUCTION 65
3.2 CHALLENGE 1: PROBLEM DEFINITION 6 6
3.2.1 OPTIMIZATION IN UNCERTAIN ENVIRONMENTS 6 6
3.2.2 PROBLEM DEFINITIONS 68
3.2.3 CHARACTERISATION O F DYNAMICS 69
3.2.4 PROBLEM PROPERTIES, ASSUMPTIONS AND GENERALISATIONS 7 0
3.3 CHALLENGE II: BENCHMARK PROBLEMS 71
3.3.1 BENCHMARK PROBLEMS 71
3.3.2 COMBINATORIAL FITNESS LANDSCAPES 7 2
3.3.3 REAL-WORLD DYNAMICS 73
3.3.4 EXPERIMENTAL SETTINGS 74
3.4 CHALLENGE III: NOTIONS OF OPTIMALITY 75
3.4.1 PERFORMANCE MEASURES IN EVOLUTIONARY DYNAMIC OPTIMIZATION 75
3.4.2 EXISTENCE O F A MODEL 77
3.4.3 NOTIONS OF OPTIMALITY 77
3.5 IMPLICATIONS, PERSPECTIVES AND CONCLUSIONS 79
3.5.1 SUMMARY 79
3.5.2 IMPLICATIONS AND PERSPECTIVES 80
3.5.3 CONCLUSIONS 80
REFERENCES 81
IMAGE 3
CONTENTS
X V I I
4 DYNAMIC MULTI-OBJECTIVE OPTIMIZATION: A SURVEY O F THE
STATE-OF-THE-ART 85
CARLO RAQUEL, XIN YAO 4. 1 INTRODUCTION 85
4.2 COMPREHENSIVE DEFINITION O F DYNAMIC MULTI-OBJECTIVE OPTIMIZATION 86
4.3 DYNAMIC MULTI-OBJECTIVE TEST PROBLEMS 88
4.3.1 DYNAMIC MULTI-OBJECTIVE OPTIMIZATION TEST PROBLEMS 90
4.4 PERFORMANCE MEASURES 9 0
4.4.1 PERFORMANCE MEASURES FOR PROBLEMS WITH KNOWN PARETO FRONT 92
4.4.2 PERFORMANCE MEASURES FOR PROBLEMS WITH UNKNOWN PARETO FRONTS 95
4.5 DYNAMIC MULTI-OBJECTIVE OPTIMIZATION APPROACHES 97
4.5.1 DIVERSITY INTRODUCTION 97
4.5.2 DIVERSITY MAINTENANCE 99
4.5.3 MULTIPLE POPULATIONS 100
4.5.4 PREDICTION-BASED APPROACHES 101
4.5.5 MEMORY-BASED APPROACHES 102
4.6 SUMMARY AND FUTURE WORKS 103
REFERENCES 104
PART II: ALGORITHM DESIGN
5 A COMPARATIVE STUDY ON PARTICLE SWARM OPTIMIZATION IN DYNAMIC
ENVIRONMENTS 109
CHANGHE LI, SHENGXIANG YANG 5.1 INTRODUCTION 109
5.2 PSO IN DYNAMIC ENVIRONMENTS 110
5.2.1 PARTICLE SWARM OPTIMIZATION 110
5.2.2 PSO IN DYNAMIC ENVIRONMENTS I L L
5.3 DISCUSSIONS AND SUGGESTIONS 118
5.3.1 ISSUES WITH CURRENT SCHEMES 118
5.3.2 FUTURE ALGORITHMS FOR DOPS 120
5.4 EXPERIMENTAL STUDY 121
5.4.1 EXPERIMENTAL SETUP 122
5.4.2 EFFECT ON VARYING THE SHIT LENGTH 124
5.4.3 EFFECT ON VARYING THE NUMBER O F PEAKS 126
5.4.4 EFFECT ON VARYING THE NUMBER O F DIMENSIONS 128
5.4.5 COMPARISON IN HARD-TO-DETECT ENVIRONMENTS 130
5.5 CONCLUSIONS 132
REFERENCES 133
IMAGE 4
XVIII
CONTENTS
6 MEMETIC ALGORITHMS FOR DYNAMIC OPTIMIZATION PROBLEMS 137
HONGFENG WANG, SHENGXIANG YANG 6.1 INTRODUCTION 137
6.2 INVESTIGATED ALGORITHMS 139
6.2.1 FRAMEWORK O F GA-BASED MEMETIC ALGORITHMS 139
6.2.2 LOCAL SEARCH 140
6.2.3 ADAPTIVE LEARNING MECHANISM IN MULTIPLE LS OPERATORS 143
6.2.4 DIVERSITY MAINTAINING 145
6.2.5 BALANCE BETWEEN LOCAL SEARCH AND DIVERSITY MAINTAINING 147
6.3 DYNAMIC TEST ENVIRONMENTS 148
6.4 EXPERIMENTAL STUDY 150
6.4.1 EXPERIMENTAL DESIGN 150
6.4.2 EXPERIMENTAL STUDY ON THE EFFECT O F LS OPERATORS 152
6.4.3 EXPERIMENTAL STUDY ON THE EFFECT OF DIVERSITY MAINTAINING SCHEMES
155
6.4.4 EXPERIMENTAL STUDY ON COMPARING THE PROPOSED ALGORITHM WITH
SEVERAL PEER GAS ON DOPS 159
6.5 CONCLUSIONS AND FUTURE WORK 164
REFERENCES 168
7 BIPOP: A NEW ALGORITHM WITH EXPLICIT EXPLORATION/EXPLOITATION CONTROL
FOR DYNAMIC OPTIMIZATION PROBLEMS 171
ENRIQUE ALBA, HAJER BEN-ROMDHANE, SAOUSSEN KRICHEN, BRISEIDA SARASOLA
7.1 INTRODUCTION 172
7.2 STATEMENT O F THE PROBLEM 173
7.3 THE PROPOSED APPROACH: BIPOP-ALGORITHM 174
7.3.1 WORKING PRINCIPLES O F BIPOP 175
7.3.2 CONSTRUCTION O F BIPOP 178
7.3.3 FUNCTIONS UTILIZED IN THE ALGORITHMS 179
7.4 COMPUTATIONAL EXPERIMENTS 179
7.4.1 EXPERIMENTAL FRAMEWORK 179
7.4.2 ANALYSIS 180
7.5 CONCLUSIONS 189
REFERENCES 189
8 EVOLUTIONARY OPTIMIZATION ON CONTINUOUS DYNAMIC CONSTRAINED PROBLEMS -
A N ANALYSIS 193
TRUNG THANH NGUYEN, XIN YAO 8.1 INTRODUCTION 193
8.2 CHARACTERISTICS O F REAL-WORLD DYNAMIC CONSTRAINED PROBLEMS 194
8.3 A REAL-VALUED BENCHMARK TO SIMULATE DCOPS CHARACTERISTICS 195
IMAGE 5
CONTENTS X I X
8.3.1 RELATED LITERATURE 195
8.3.2 GENERATING DYNAMIC CONSTRAINED BENCHMARK PROBLEMS 196
8.3.3 A DYNAMIC CONSTRAINED BENCHMARK SET 196
8.4 CHALLENGES TO SOLVE DCOPS 200
8.4.1 ANALYSING THE PERFORMANCE OF SOME COMMON DYNAMIC OPTIMIZATION
STRATEGIES IN SOLVING DCOPS . . . 200 8.4.2 CHOSEN ALGORITHMS AND
EXPERIMENTAL SETTINGS 202
8.4.3 EXPERIMENTAL RESULTS AND ANALYSES 207
8.4.4 SUGGESTIONS TO IMPROVE CURRENT DYNAMIC OPTIMIZATION STRATEGIES IN
SOLVING DCOPS 213
8.5 CONCLUSION AND FUTURE RESEARCH 214
REFERENCES 215
PART III: THEORETICAL ANALYSIS
9 THEORETICAL ADVANCES IN EVOLUTIONARY DYNAMIC OPTIMIZATION 221 PHILIPP
ROHLFSHAGEN, PER KRISTIAN LEHRE, XIN YAO 9.1 INTRODUCTION 221
9.2 EVOLUTIONARY DYNAMIC OPTIMIZATION 222
9.2.1 OPTIMIZATION PROBLEMS 222
9.2.2 OPTIMIZATION IN UNCERTAIN ENVIRONMENTS 223
9.2.3 EVOLUTIONARY ALGORITHMS 224
9.3 THEORETICAL FOUNDATION 224
9.3.1 INTRODUCTION TO RUNTIME ANALYSIS 224
9.3.2 RUNTIME ANALYSIS FOR DYNAMIC FUNCTIONS 226
9.3.3 N O FREE LUNCHES IN THE DYNAMIC DOMAIN 227
9.3.4 BENCHMARK PROBLEMS 228
9.4 RUNTIME ANALYSIS FOR DYNAMIC FUNCTIONS 231
9.4.1 FIRST HITTING TIMES FOR PATTERN MATCH 231
9.4.2 ANALYSIS O F FREQUENCY AND MAGNITUDE O F CHANGE 232
9 . 4 . 3 TRACKING THE OPTIMUM IN A LATTICE 2 3 4
9.5 CONCLUSIONS 235
9.5.1 SUMMARY AND IMPLICATIONS 235
9.5.2 FUTURE WORK 236
REFERENCES 237
10 ANALYZING EVOLUTIONARY ALGORITHMS FOR DYNAMIC OPTIMIZATION PROBLEMS
BASED ON THE DYNAMICAL SYSTEMS APPROACH 241
RENATO TINDS, SHENGXIANG YANG 10.1 INTRODUCTION 241
10.2 EXACT MODEL OF THE GA IN STATIONARY ENVIRONMENTS 242
10.3 DYNAMIC OPTIMIZATION PROBLEMS 245
10.4 EXAMPLES 249
IMAGE 6
X X
CONTENTS
10.4.1 THE XOR DOP GENERATOR 249
10.4.2 THE DYNAMIC ENVIRONMENT GENERATOR BASED ON PROBLEM DIFFICULTY 253
10.4.3 THE DYNAMIC 0-1 KNAPSACK PROBLEM 256
10.5 CONCLUSION AND FUTURE WORK 265
REFERENCES 265
11 DYNAMIC FITNESS LANDSCAPE ANALYSIS 269
HENDRIK RICHTER 11.1 INTRODUCTION 269
11.2 DYNAMIC FITNESS LANDSCAPES: DEFINITIONS AND PROPERTIES 271 11.2.1
INTRODUCTORY EXAMPLE: THE MOVING PEAKS 271
11.2.2 DEFINITION O F DYNAMIC FITNESS LANDSCAPES 273
11.2.3 DYNAMICS AND FITNESS LANDSCAPES 276
11.3 ANALYSIS TOOLS FOR DYNAMIC FITNESS LANDSCAPES 279
11.3.1 ANALYSIS O F TOPOLOGICAL PROPERTIES . . . ' 280
11.3.2 ANALYSIS O F DYNAMICAL PROPERTIES 283
11.4 NUMERICAL EXPERIMENTS 286
11.5 CONCLUSION 293
REFERENCES 294
12 DYNAMICS IN THE MULTI-OBJECTIVE SUBSET SUM: ANALYSING THE BEHAVIOR O
F POPULATION BASED ALGORITHMS 299
LULIA MARIA COMSA, CRINA GROSAN, SHENGXIANG YANG 12.1 INTRODUCTION : 299
12.2 DYNAMIC OPTIMIZATION 300
12.3 MULTI-OBJECTIVE ASPECT 302
12.4 THE MULTI-OBJECTIVE SUBSET SUM PROBLEM 304
12.5 ANALYSIS O F THE DYNAMIC MULTI-OBJECTIVE SUBSET SUM PROBLEM . . 304
12.5.1 ALGORITHM DESCRIPTION 305
12.5.2 NUMERICAL RESULTS AND DISCUSSIONS 306
12.6 CONCLUSIONS 309
REFERENCES 312
PART IV: APPLICATIONS
13 ANT COLONY OPTIMIZATION ALGORITHMS WITH IMMIGRANTS SCHEMES FOR THE
DYNAMIC TRAVELLING SALESMAN PROBLEM 317
MICHALIS MAVROVOUNIOTIS, SHENGXIANG YANG 13.1 INTRODUCTION 317
13.2 DYNAMIC TRAVELLING SALESMAN PROBLEM WITH TRAFFIC FACTOR 319 13.2.1
DTSP WITH RANDOM TRAFFIC 319
13.2.2 DTSP WITH CYCLIC TRAFFIC 320
13.3 ANT COLONY OPTIMIZATION FOR THE DTSP 320
IMAGE 7
CONTENTS X X I
13.3.1 STANDARD ACO 321
13.3.2 POPULATION-BASED ACO (P-ACO) 322
13.3.3 REACT TO DYNAMIC CHANGES 322
13.4 INVESTIGATED ACO ALGORITHMS WITH IMMIGRANTS SCHEMES 323 13.4.1
GENERAL FRAMEWORK O F ACO WITH IMMIGRANTS SCHEMES 323
13.4.2 ACO WITH RANDOM IMMIGRANTS 325
13.4.3 ACO WITH ELITISM-BASED IMMIGRANTS 325
13.4.4 ACO WITH HYBRID IMMIGRANTS 326
13.4.5 ACO WITH MEMORY-BASED IMMIGRANTS 326
13.4.6 ACO WITH ENVIRONMENTAL-INFORMATION IMMIGRANTS 327 13.5
EXPERIMENTS 328
13.5.1 EXPERIMENTAL SETUP 328
13.5.2 PARAMETER SETTINGS 329
13.5.3 EXPERIMENTAL RESULTS AND ANALYSIS O F THE INVESTIGATED ALGORITHMS
329
13.5.4 EXPERIMENTAL RESULTS AND ANALYSIS OF THE INVESTIGATED ALGORITHMS
WITH OTHER PEER ACO 335
13.6 CONCLUSIONS AND FUTURE WORK 338
REFERENCES 339
14 GENETIC ALGORITHMS FOR DYNAMIC ROUTING PROBLEMS IN MOBILE A D HOC
NETWORKS 343
HUI CHENG, SHENGXIANG YANG 14.1 INTRODUCTION 343
14.2 RELATED WORK 346
14.2.1 SHORTEST PATH ROUTING 346
14.2.2 MULTICAST ROUTING 347
14.3 NETWORK AND PROBLEM MODELS 348
14.3.1 MOBILE AD HOC NETWORK MODEL 348
14.3.2 DYNAMIC SHORTEST PATH ROUTING PROBLEM MODEL 348
14.3.3 DYNAMIC MULTICAST ROUTING PROBLEM MODEL 349
14.4 SPECIALIZED GAS FOR THE ROUTING PROBLEMS 350
14.4.1 SPECIALIZED GA FOR THE SHORTEST PATH ROUTING PROBLEM 350
14.4.2 SPECIALIZED GA FOR THE MULTICAST ROUTING PROBLEM . . . . 352 14.5
INVESTIGATED GAS FOR THE DYNAMIC ROUTING PROBLEMS 354
14.5.1 TRADITIONAL GAS 354
14.5.2 GAS WITH IMMIGRANTS SCHEMES 354
14.5.3 IMPROVED GAS WITH IMMIGRANTS SCHEMES 355
14.5.4 GAS WITH MEMORY SCHEMES 356
14.5.5 GAS WITH MEMORY AND IMMIGRANTS SCHEMES 356
14.6 EXPERIMENTAL STUDY 357
14.6.1 DYNAMIC TEST ENVIRONMENT 357
14.6.2 EXPERIMENTAL STUDY FOR THE DSPRP 357
IMAGE 8
XXII CONTENTS
14.6.3 EXPERIMENTAL STUDY FOR THE DMRP 364
14.7 CONCLUSION 372
REFERENCES 372
15 EVOLUTIONARY COMPUTATION FOR DYNAMIC CAPACITATED ARC ROUTING PROBLEM
377
YI MEI, KE TANG, XIN YAO 15.1 INTRODUCTION 378
15.2 PROBLEM DEFINITION 380
15.2.1 STATIC CAPACITATED ARC ROUTING PROBLEM 380
15.2.2 DYNAMIC CAPACITATED ARC ROUTING PROBLEM 381
15.3 EVOLUTIONARY COMPUTATION FOR DYNAMIC CAPACITATED ARC ROUTING
PROBLEM 386
15.3.1 ADDRESSING THE CAPACITATED ARC ROUTING PROBLEM ISSUES 386
15.3.2 TACKLING THE DYNAMIC ENVIRONMENT . . : 392
15.4 BENCHMARK FOR DYNAMIC CAPACITATED ARC ROUTING PROBLEM 393 15.5
PRELIMINARY INVESTIGATION O F THE FITNESS LANDSCAPE 396
15.6 CONCLUSION 398
REFERENCES 399
16 EVOLUTIONARY ALGORITHMS FOR THE MULTIPLE UNMANNED AERIAL COMBAT
VEHICLES ANTI-GROUND ATTACK PROBLEM IN DYNAMIC ENVIRONMENTS 403
XINGGUANG PENG, SHENGXIANG YANG, DEMIN XU, XIAOGUANG GAO 16.1
INTRODUCTION 404
16.2 INTELLIGENT ONLINE PATH PLANNING (OPP) 405
16.2.1 FORMULATION O F THE OPP PROBLEM 406
16.2.2 PROBLEM-SOLVING APPROACH: LP-DMOEA 407
16.2.3 DECISION-MAKING ON THE SELECTION OF EXECUTIVE SOLUTION 410
16.3 DYNAMIC TARGET ASSIGNMENT 413
16.3.1 FORMULATION O F THE DYNAMIC WTA PROBLEM 413
16.3.2 PROBLEM-SOLVING APPROACH: MEMORY-BASED ESTIMATION O F
DISTRIBUTION ALGORITHM WITH ENVIRONMENT IDENTIFICATION 416
16.3.3 CHROMOSOME REPRESENTATION 420
16.3.4 WEAPON-UCAV MAPPING 420
16.4 SIMULATION RESULTS AND ANALYSIS 420
16.4.1 SIMULATION SCENARIO 420
16.4.2 RESULTS AND ANALYSIS ON THE INTELLIGENT OPP P R O B L E M . . .
423 16.4.3 RESULTS AND ANALYSIS ON THE DYNAMIC WTA PROBLEM . . . 427
16.5 CONCLUSIONS AND FUTURE WORK 429
REFERENCES 430
IMAGE 9
CONTENTS XXIII
17 ADVANCED PLANNING IN VERTICALLY INTEGRATED WINE SUPPLY
CHAINS 433
MAKSUD IBRAHIMOV, ARVIND MOHAIS, MARIS OZOLS, SVEN SCHELLENBERG,
ZBIGNIEW MICHALEWICZ 17.1 INTRODUCTION 433
17.2 LITERATURE REVIEW 435
17.2.1 SUPPLY CHAIN MANAGEMENT 436
17.2.2 TIME-VARYING CONSTRAINTS 438
17.2.3 COMPUTATIONAL INTELLIGENCE 439
17.3 WINE SUPPLY CHAIN 440
17.3.1 MATURITY MODELS 442
17.3.2 VINTAGE INTAKE PLANNING 443
17.3.3 CRUSHING 443
17.3.4 TANK FARM 444
17.3.5 BOTTLING 444
17.4 VINTAGE INTAKE PLANNING 444
17.4.1 DESCRIPTION OF THE PROBLEM 444
17.4.2 CONSTRAINTS 446
17.5 TANK FARM 447
17.5.1 DESCRIPTION OF THE PROBLEM 447
17.5.2 FUNCTIONALITY 449
17.5.3 RESULTS 452
17.6 BOTTLING 453
17.6.1 TIME-VARYING CHALLENGES IN WINE BOTTLING 455
17.6.2 OBJECTIVE 457
17.6.3 THE ALGORITHM 457
17.7 CONCLUSION 460
REFERENCES 462
AUTHOR INDEX 465
SUBJECT INDEX 467 |
| any_adam_object | 1 |
| author2 | Yang, Shengxiang |
| author2_role | edt |
| author2_variant | s y sy |
| author_GND | (DE-588)132988127 |
| author_facet | Yang, Shengxiang |
| building | Verbundindex |
| bvnumber | BV041169569 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)854920601 (DE-599)DNB1033706876 |
| dewey-full | 519.703 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.703 |
| dewey-search | 519.703 |
| dewey-sort | 3519.703 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik |
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| id | DE-604.BV041169569 |
| illustrated | Illustrated |
| indexdate | 2024-09-10T00:59:22Z |
| institution | BVB |
| isbn | 9783642384158 9783642384165 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026144800 |
| oclc_num | 854920601 |
| open_access_boolean | |
| owner | DE-11 DE-83 |
| owner_facet | DE-11 DE-83 |
| physical | XXVIII, 470 S. Ill., graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Evolutionary computation for dynamic optimization problems Shengxiang Yang ... ed. Berlin [u.a.] Springer 2013 XXVIII, 470 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 490 Dynamische Optimierung (DE-588)4125677-3 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Dynamische Optimierung (DE-588)4125677-3 s Evolutionärer Algorithmus (DE-588)4366912-8 s DE-604 Yang, Shengxiang (DE-588)132988127 edt Studies in computational intelligence 490 (DE-604)BV020822171 490 text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4301491&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026144800&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Evolutionary computation for dynamic optimization problems Studies in computational intelligence Dynamische Optimierung (DE-588)4125677-3 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| subject_GND | (DE-588)4125677-3 (DE-588)4366912-8 |
| title | Evolutionary computation for dynamic optimization problems |
| title_auth | Evolutionary computation for dynamic optimization problems |
| title_exact_search | Evolutionary computation for dynamic optimization problems |
| title_full | Evolutionary computation for dynamic optimization problems Shengxiang Yang ... ed. |
| title_fullStr | Evolutionary computation for dynamic optimization problems Shengxiang Yang ... ed. |
| title_full_unstemmed | Evolutionary computation for dynamic optimization problems Shengxiang Yang ... ed. |
| title_short | Evolutionary computation for dynamic optimization problems |
| title_sort | evolutionary computation for dynamic optimization problems |
| topic | Dynamische Optimierung (DE-588)4125677-3 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| topic_facet | Dynamische Optimierung Evolutionärer Algorithmus |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4301491&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026144800&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT yangshengxiang evolutionarycomputationfordynamicoptimizationproblems |