Experimental methods for the analysis of optimization algorithms:
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| Format: | Book |
| Language: | English |
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Berlin [u.a.]
Springer
2010
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| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XXII, 457 S. graph. Darst. 24 cm |
| ISBN: | 9783642025372 |
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| 245 | 1 | 0 | |a Experimental methods for the analysis of optimization algorithms |c Thomas Bartz-Beielstein ... ed. |
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IMAGE 1
CONTENTS
1 INTRODUCTION 1
THOMAS BARTZ-BEIELSTEIN, MARCO CHIARANDINI, LUIS PAQUETE, AND MIKE
PREUSS 1.1 OPTIMIZATION ALGORITHMS 1
1.2 ANALYSIS OF ALGORITHMS 3
1.2.1 THEORETICAL ANALYSIS 3
1.2.2 EXPERIMENTAL ANALYSIS 3
1.3 BRIDGING THE GAP BETWEEN THEORETICAL AND EMPIRICAL ANALYSIS . . 5
1.4 THE NEED FOR STATISTICS 7
1.5 BOOK CONTENTS 8
REFERENCES 12
PARTI OVERVIEW
2 THE FUTURE OF EXPERIMENTAL RESEARCH 17
THOMAS BARTZ-BEIELSTEIN AND MIKE PREUSS 2.1 INTRODUCTION 17
2.2 EXPERIMENTAL GOALS IN COMPUTER SCIENCE 19
2.2.1 IMPROVING THE PERFORMANCE 20
2.2.2 UNDERSTANDING 21
2.3 PROBLEMS 21
2.3.1 PROBLEMS RELATED TO THE EXPERIMENTAL SETUP 22
2.3.2 PROBLEMS RELATED TO THE SIGNIFICANCE OF EXPERIMENTAL RESULTS 22
2.3.3 PROBLEMS RELATED TO HIGH-LEVEL THEORY 25
2.4 THE NEW EXPERIMENTALISM 27
2.5 EXPERIMENTAL MODELS 28
2.5.1 THE ROLE OF MODELS IN SCIENCE 28
2.5.2 REPRESENTATIONAL MODELS 29
2.5.3 A FRAMEWORK OF MODELS 30
2.5.4 MAYO'S LEARNING MODEL 32
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1000033163
DIGITALISIERT DURCH
IMAGE 2
XII CONTENTS
2.5.5 SEQUENTIAL PARAMETER OPTIMIZATION 34
2.5.6 THE LARGE N PROBLEM REVISITED 38
2.6 PITFALLS OF EXPERIMENTATION WITH RANDOMIZED ALGORITHMS 38 2.6.1
FLOOR AND CEILING EFFECTS 39
2.6.2 CONFOUNDED EFFECTS 40
2.6.3 FAIRNESS IN PARAMETER SETTINGS 40
2.6.4 FAILURE REASONS AND PREVENTION 41
2.7 TOOLS: MEASURES AND REPORTS 42
2.7.1 MEASURES 42
2.7.2 REPORTING EXPERIMENTS 44
2.8 METHODOLOGY, OPEN ISSUES, AND DEVELOPMENT 45
2.9 SUMMARY 46
REFERENCES 47
3 DESIGN AND ANALYSIS OF COMPUTATIONAL EXPERIMENTS: OVERVIEW 51 JACK
P.C. KLEIJNEN 3.1 INTRODUCTION 51
3.2 CLASSIC DESIGNS AND METAMODELS 54
3.3 SCREENING: SEQUENTIAL BIFURCATION 57
3.4 KRIGING 58
3.4.1 THE KRIGING PREDICTOR VARIANCE 60
3.4.2 DESIGNS FOR KRIGING 61
3.5 OPTIMIZATION 63
3.5.1 RESPONSE SURFACE METHODOLOGY (RSM) 63
3.5.2 KRIGING AND MATHEMATICAL PROGRAMMING 65
3.5.3 TAGUCHIAN ROBUST OPTIMIZATION 67
3.6 CONCLUSIONS 69
REFERENCES 70
4 THE GENERATION OF EXPERIMENTAL DATA FOR COMPUTATIONAL TESTING IN
OPTIMIZATION 73
NICHOLAS G. HALL AND MARC E. POSNER 4.1 INTRODUCTION 73
4.2 A PROTOCOL 76
4.2.1 GENERATION PRINCIPLES AND PROPERTIES 78
4.2.2 FEATURES OF THE MODEL 81
4.2.3 VALIDATING THE DATA 81
4.3 APPLICATIONS TO OPTIMIZATION 83
4.3.1 GENERALIZED ASSIGNMENT AND KNAPSACK 84
4.3.2 SUPPLY CHAINS 85
4.3.3 SCHEDULING 87
4.3.4 GRAPHS AND NETWORKS 88
4.3.5 ROUTING 89
4.3.6 DATA MINING 90
4.3.7 STOCHASTIC PROGRAMMING 91
4.3.8 INTRACTABLE PROBLEMS 92
IMAGE 3
CONTENTS XIUE
4.4 CONCLUDING REMARKS 94
REFERENCES 94
5 THE ATTAINMENT-FUNCTION APPROACH TO STOCHASTIC MULTIOBJECTIVE
OPTIMIZER ASSESSMENT AND COMPARISON 103
VIVIANE GRUNERT DA FONSECA AND CARLOS M. FONSECA 5.1 INTRODUCTION 103
5.2 STATISTICS AND THE ATTAINMENT-FUNCTION APPROACH 105
5.2.1 STOCHASTIC OPTIMIZERS AS STATISTICAL ESTIMATORS 105 5.2.2
OPTIMIZER PERFORMANCE AS STATISTICAL ESTIMATOR PERFORMANCE 106
5.2.3 PERFORMANCE ASSESSMENT VIA ESTIMATION AND HYPOTHESIS TESTING 108
5.3 MULTIOBJECTIVE OPTIMIZER OUTCOMES 109
5.3.1 RANDOM NONDOMINATED POINT SETS 109
5.3.2 ALTERNATIVE VIEW: THE ATTAINED SET 109
5.4 MULTIOBJECTIVE OPTIMIZER PERFORMANCE 110
5.4.1 DISTRIBUTION OF A GENERAL RANDOM CLOSED SET: THE CAPACITY
FUNCTIONAL I LL
5.4.2 DISTRIBUTION OF A RANDOM NONDOMINATED POINT SET: THE FC-TH-ORDER
ATTAINMENT FUNCTION I LL
5.5 PARTIAL ASPECTS OF MULTIOBJECTIVE OPTIMIZER PERFORMANCE 113 5.5.1
DISTRIBUTION LOCATION: THE FIRST-ORDER ATTAINMENT FUNCTION 113
5.5.2 DISTRIBUTION SPREAD: THE VARIANCE FUNCTION 116 5.5.3 INTER-POINT
DEPENDENCE STRUCTURES: SECOND AND HIGHER-ORDER ATTAINMENT FUNCTIONS 117
5.6 MULTIOBJECTIVE OPTIMIZER PERFORMANCE ASSESSMENT: ESTIMATION. 119
5.7 MULTIOBJECTIVE OPTIMIZER PERFORMANCE COMPARISON: HYPOTHESIS TESTING
121
5.7.1 TWO-SIDED TEST PROBLEM 122
5.7.2 PERMUTATION TEST PROCEDURE 123
5.7.3 MULTISTAGE TESTING 124
5.7.4 ONE-SIDED TESTS 126
5.8 DISCUSSION AND FUTURE PERSPECTIVES 127
REFERENCES 128
6 ALGORITHM ENGINEERING: CONCEPTS AND PRACTICE 131
MARKUS CHIMANI AND KARSTEN KLEIN 6.1 WHY ALGORITHM ENGINEERING? 131
6.1.1 EARLY DAYS AND THE PEN-AND-PAPER ERA 132
6.1.2 ERRORS 133
6.2 THE ALGORITHM ENGINEERING CYCLE 135
6.3 CURRENT TOPICS AND ISSUES 137
6.3.1 PROPERTIES OF AND STRUCTURES IN THE INPUT 138
6.3.2 LARGE DATASETS 139
IMAGE 4
XJV CONTENTS
6.3.3 MEMORY EFFICIENCY 140
6.3.4 DISTRIBUTED SYSTEMS AND PARALLELISM 143
6.3.5 APPROXIMATIONS AND HEURISTIC ALGORITHMS 144
6.3.6 SUCCINCT DATA STRUCTURES 145
6.3.7 TIME-CRITICAL SETTINGS 146
6.3.8 ROBUSTNESS 146
6.4 SUCCESS STORIES * 147
6.4.1 SHORTEST PATH COMPUTATION 147
6.4.2 FULL-TEXT INDEXES 150
6.5 SUMMARY AND OUTLOOK 153
REFERENCES 154
PART II CHARACTERIZING ALGORITHM PERFORMANCE
7 ALGORITHM SURVIVAL ANALYSIS 161
MATTEO GAGLIOLO AND CATHERINE LEGRAND 7.1 INTRODUCTION 161
7.2 MODELING RUNTIME DISTRIBUTIONS 162
7.2.1 BASIC QUANTITIES AND CONCEPTS 163
7.2.2 CENSORING 165
7.2.3 ESTIMATION IN SURVIVAL ANALYSIS 166
7.2.4 COMPETING RISKS 169
7.3 MODEL-BASED ALGORITHM SELECTION 171
7.3.1 RTD OF AN ALGORITHM PORTFOLIO 173
7.3.2 MODEL-BASED TIME ALLOCATORS 174
7.3.3 ALGORITHMS AS COMPETING RISKS 175
7.4 EXPERIMENTS 175
7.5 RELATED WORK 179
7.6 SUMMARY AND OUTLOOK 180
REFERENCES 181
8 ON APPLICATIONS OF EXTREME VALUE THEORY IN OPTIMIZATION 185 JIIRG
HIISLER 8.1 INTRODUCTION 185
8.2 EXTREME VALUE THEORY 186
8.2.1 EXTREME VALUE THEORY FOR MINIMA 186
8.2.2 PEAKS OVER THRESHOLD METHOD (POT) FOR MINIMA 189 8.2.3 ASSESSMENT
OF AN OPTIMIZER 191
8.3 EXPERIMENTS USING RANDOM SEARCH 192
8.3.1 SAMPLES WITH SIMULATIONS NEAR THE OPTIMUM 193 8.3.2 SAMPLES WITH
SIMULATIONS AWAY FROM THE OPTIMUM 194 8.4 ANALYTICAL RESULTS 195
8.5 EXPERIMENTS USING EVOLUTION STRATEGIES 199
8.6 SUMMARY 205
REFERENCES 206
IMAGE 5
CONTENTS XV
9 EXPLORATORY ANALYSIS OF STOCHASTIC LOCAL SEARCH ALGORITHMS IN
BIOBJECTIVE OPTIMIZATION 209
MANUEL LOPEZ-IBANEZ, LUIS PAQUETE, AND THOMAS STUETZLE 9.1 INTRODUCTION
209
9.2 STOCHASTIC LOCAL SEARCH FOR MULTIOBJECTIVE PROBLEMS 210 9.3
EXAMINATION OF THE ATTAINMENT SURFACES 212
9.3.1 THE E A F P L OT . PI PROGRAM 213
9.3.2 EXAMPLE APPLICATION OF E A F P L OT . PI 214
9.4 EXAMINING THE DIFFERENCES BETWEEN EAFS 215
9.5 EXAMPLES 217
9.5.1 EFFECT OF PROBLEM STRUCTURE 217
9.5.2 DIFFERENCES IN ALGORITHMIC PERFORMANCE 218
9.5.3 BIASED BEHAVIOR 219
9.6 SUMMARY AND OUTLOOK 220
REFERENCES 221
PART III ALGORITHM CONFIGURATION AND TUNING
10 MIXED MODELS FOR THE ANALYSIS OF OPTIMIZATION ALGORITHMS 225 MARCO
CHIARANDINI AND YURI GOEGEBEUR 10.1 INTRODUCTION 225
10.2 EXPERIMENTAL DESIGNS AND STATISTICAL MODELING 228
10.2.1 CASE (-, ?(-), R): RANDOM-EFFECTS DESIGN 229
10.2.2 CASE {N, Q(-), R): MIXED-EFFECTS DESIGN 233
10.2.3 CASE (-, Q(M), R): NESTED-EFFECTS DESIGN 236
10.2.4 CASE {N, Q(M),R): GENERAL MIXED-EFFECTS DESIGN 237 10.3 AN
APPLICATION EXAMPLE IN OPTIMIZATION HEURISTIC DESIGN 240 10.3.1
DEFINITIONS AND PROBLEM FORMULATION 240
10.3.2 LOCAL SEARCH ALGORITHMS 241
10.3.3 PROBLEM INSTANCES 242
10.4 NUMERICAL EXAMPLES 242
10.4.1 CASE (-, Q(-),R): RANDOM-EFFECTS DESIGN 243
10.4.2 CASE (N, Q(-), R): MIXED-EFFECTS DESIGN 247
10.4.3 CASE (-, Q(M), R): NESTED DESIGN 256
10.4.4 CASE (N, Q{M),R): GENERAL DESIGN 258
10.5 SUMMARY AND OUTLOOK 260
REFERENCES 262
11 TUNING AN ALGORITHM USING DESIGN OF EXPERIMENTS 265
ENDA RIDGE AND DANIEL KUDENKO 11.1 INTRODUCTION 265
11.2 RESEARCH QUESTIONS ADDRESSED WITH DOE 266
11.3 EXPERIMENT DESIGNS 266
11.3.1 FULL AND 2 FC FACTORIAL DESIGNS 267
11.3.2 FRACTIONAL FACTORIAL DESIGNS 267
11.3.3 RESPONSE SURFACE DESIGNS 270
IMAGE 6
XVI CONTENTS
11.3.4 EFFICIENCY OF FRACTIONAL FACTORIAL DESIGNS 271 11.4 ERROR,
SIGNIFICANCE, POWER, AND REPLICATES 271
11.5 BENCHMARKING THE EXPERIMENTAL TESTBED 272
11.6 CASE STUDY 273
11.6.1 PROBLEM INSTANCES 273
11.6.2 STOPPING CRITERION 274
11.6.3 RESPONSE VARIABLES 274
11.6.4 FACTORS, LEVELS AND RANGES 274
11.6.5 MODEL FITTING 277
11.6.6 RESULTS 279
11.6.7 DISCUSSION 284
11.6.8 SUMMARY 285
REFERENCES 285
12 USING ENTROPY FOR PARAMETER ANALYSIS OF EVOLUTIONARY ALGORITHMS .
287 SELMAR K. SMIT AND AGOSTON E. EIBEN 12.1 INTRODUCTION AND BACKGROUND
287
12.2 EVOLUTIONARY ALGORITHMS 288
12.3 EA DESIGN, EA PARAMETERS 291
12.4 SHANNON AND DIFFERENTIAL ENTROPY 294
12.4.1 USING SUCCESS RANGES FOR RELEVANCE ESTIMATION 294 12.4.2 SHANNON
ENTROPY 295
12.4.3 USING THE SHANNON ENTROPY FOR RELEVANCE ESTIMATION . . 295
12.4.4 DIFFERENTIAL ENTROPY 297
12.4.5 JOINT ENTROPY 298
12.5 ESTIMATING ENTROPY 298
12.5.1 REVAC: THE ALGORITHM 300
12.5.2 REVAC: THE DATA GENERATED 301
12.6 CASE STUDY 302
12.6.1 EXPERIMENTAL SETUP 303
12.6.2 ENTROPY OF PARAMETERS 304
12.6.3 ENTROPY OF OPERATORS 305
12.6.4 ENTROPY OF EAS 306
12.7 CONCLUSIONS 307
REFERENCES 308
13 F-RACE AND ITERATED F-RACE: AN OVERVIEW 311
MAURO BIRATTARI, ZHI YUAN, PRASANNA BALAPRAKASH, AND THOMAS STIITZLE
13.1 INTRODUCTION 311
13.2 THE ALGORITHM CONFIGURATION PROBLEM 313
13.2.1 THE ALGORITHM CONFIGURATION PROBLEM 313
13.2.2 TYPES OF PARAMETERS 315
13.3 F-RACE 316
13.3.1 THE RACING APPROACH 316
13.3.2 THE PECULIARITY OF F-RACE 317
13.4 THE SAMPLING STRATEGY FOR F-RACE 320
IMAGE 7
CONTENTS XVII
13.4.1 FULL FACTORIAL DESIGN 320
13.4.2 RANDOM SAMPLING DESIGN 320
13.4.3 ITERATED F-RACE 321
13.4.4 AN EXAMPLE ITERATED F-RACE ALGORITHM 323
13.5 CASE STUDIES 325
13.5.1 CASE STUDY 1 : MMAS UNDER FOUR PARAMETERS 326 13.5.2 CASE STUDY
2: MMAS UNDER SEVEN PARAMETERS 327 13.5.3 CASE STUDY 3: ACOTSP UNDER 12
PARAMETERS 328 13.6 A REVIEW OF F-RACE APPLICATIONS 329
13.7 SUMMARY AND OUTLOOK 332
REFERENCES 332
14 THE SEQUENTIAL PARAMETER OPTIMIZATION TOOLBOX 337
THOMAS BARTZ-BEIELSTEIN, CHRISTIAN LASARCZYK, AND MIKE PREUSS 14.1
INTRODUCTION 337
14.2 APPLICATIONS 338
14.2.1 BIOINFORMATICS 338
14.2.2 WATER-RESOURCE MANAGEMENT 339
14.2.3 MECHANICAL ENGINEERING 339
14.2.4 BIOGAS 339
14.2.5 SHIPBUILDING 340
14.2.6 FUZZY OPERATOR TREES 340
14.3 OBJECTIVES 340
14.4 ELEMENTS OF THE SPOT FRAMEWORK 341
14.4.1 THE GENERAL SPOT SCHEME 341
14.4.2 SPOTTASKS 342
14.4.3 RUNNING SPOT 344
14.5 STATISTICAL CONSIDERATIONS 345
14.5.1 SEQUENTIAL MODELS 345
14.5.2 RESIDUALS AND VARIANCE 348
14.5.3 STANDARDIZED VARIABLES AND TRANSFORMATIONS 348 14.5.4 DESIGN
CONSIDERATIONS AND THE REGION OF INTEREST 349 14.6 CASE STUDY 350
14.6.1 PRE-EXPERIMENTAL PLANNING 350
14.6.2 PERFORMING THE FIRST REGRESSION ANALYSIS 351
14.6.3 STEEPEST DESCENT 354
14.7 ADDITIONAL MODEL CONSIDERATIONS 357
14.8 THE AUTOMATED MODE 360
14.9 SUMMARY 360
REFERENCES 361
15 SEQUENTIAL MODEL-BASED PARAMETER OPTIMIZATION: AN EXPERIMENTAL
INVESTIGATION OF AUTOMATED AND INTERACTIVE APPROACHES 363 FRANK HUTTER,
THOMAS BARTZ-BEIELSTEIN, HOLGER H. HOOS, KEVIN LEYTON-BROWN, AND KEVIN
P. MURPHY
15.1 INTRODUCTION 364
IMAGE 8
CONTENTS
15.2 TARGET ALGORITHMS AND EXPERIMENTAL SETUP 367
15.3 EXISTING METHODS FOR SEQUENTIAL MODEL-BASED OPTIMIZATION OF NOISY
FUNCTIONS 369
15.3.1 GENERAL GAUSSIAN PROCESS REGRESSION 369
15.3.2 A COMMON FRAMEWORK FOR SEQUENTIAL MODEL-BASED OPTIMIZATION 371
15.3.3 EMPIRICAL COMPARISON OF SKO AND SPO 376
15.4 MODEL QUALITY 380
15.4.1 CHOOSING THE INITIAL DESIGN 380
15.4.2 TRANSFORMING PERFORMANCE DATA 382
15.5 SEQUENTIAL EXPERIMENTAL DESIGN 383
15.5.1 INTENSIFICATION MECHANISM 384
15.5.2 EXPECTED IMPROVEMENT CRITERION 391
15.5.3 OVERALL EVALUATION 393
15.6 INTERACTIVE EXPLORATION OF PARAMETER SPACE 394
15.6.1 USING SPOT INTERACTIVELY 395
15.6.2 FURTHER INTERACTIVE TUNING RESULTS 402
15.6.3 COMPARISON OF SOLUTIONS FOUND AUTOMATICALLY AND INTERACTIVELY 407
15.6.4 DISCUSSION OF THE INTERACTIVE APPROACH 408
15.7 CONCLUSIONS AND FUTURE WORK 409
APPENDIX 410
REFERENCES 410
APPENDIX 415
A A BRIEF INTRODUCTION TO INFERENTIAL STATISTICS 417
DARIO BASSO A.I INTRODUCTION 417
A.I.I RANDOM VARIABLES 419
A.1.2 EXAMPLES OF STATISTICAL MODELS 421
A.2 POINT ESTIMATION 426
A.3 HYPOTHESIS TESTING 431
A.4 CONFIDENCE INTERVALS 440
A.5 REGRESSION AND MODELING 442
A.5.1 LINEAR REGRESSION 442
A.5.2 MODEL FITTING 447
REFERENCES 450
INDEX 453 |
| any_adam_object | 1 |
| author2 | Bartz-Beielstein, Thomas 1966- |
| author2_role | edt |
| author2_variant | t b b tbb |
| author_GND | (DE-588)124999476 |
| author_facet | Bartz-Beielstein, Thomas 1966- |
| building | Verbundindex |
| bvnumber | BV036896837 |
| classification_rvk | QH 420 SK 970 |
| ctrlnum | (OCoLC)698622642 (DE-599)DNB1000033163 |
| dewey-full | 519.6 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.6 |
| dewey-search | 519.6 |
| dewey-sort | 3519.6 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV036896837 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T10:55:17Z |
| institution | BVB |
| isbn | 9783642025372 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020811917 |
| oclc_num | 698622642 |
| open_access_boolean | |
| owner | DE-824 DE-83 DE-384 DE-2070s |
| owner_facet | DE-824 DE-83 DE-384 DE-2070s |
| physical | XXII, 457 S. graph. Darst. 24 cm |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Springer |
| record_format | marc |
| spelling | Experimental methods for the analysis of optimization algorithms Thomas Bartz-Beielstein ... ed. Berlin [u.a.] Springer 2010 XXII, 457 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Literaturangaben Optimierung (DE-588)4043664-0 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Optimierung (DE-588)4043664-0 s Algorithmus (DE-588)4001183-5 s DE-604 Bartz-Beielstein, Thomas 1966- (DE-588)124999476 edt Erscheint auch als Online-Ausgabe 978-3-642-02538-9 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3423717&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=020811917&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Experimental methods for the analysis of optimization algorithms Optimierung (DE-588)4043664-0 gnd Algorithmus (DE-588)4001183-5 gnd |
| subject_GND | (DE-588)4043664-0 (DE-588)4001183-5 |
| title | Experimental methods for the analysis of optimization algorithms |
| title_auth | Experimental methods for the analysis of optimization algorithms |
| title_exact_search | Experimental methods for the analysis of optimization algorithms |
| title_full | Experimental methods for the analysis of optimization algorithms Thomas Bartz-Beielstein ... ed. |
| title_fullStr | Experimental methods for the analysis of optimization algorithms Thomas Bartz-Beielstein ... ed. |
| title_full_unstemmed | Experimental methods for the analysis of optimization algorithms Thomas Bartz-Beielstein ... ed. |
| title_short | Experimental methods for the analysis of optimization algorithms |
| title_sort | experimental methods for the analysis of optimization algorithms |
| topic | Optimierung (DE-588)4043664-0 gnd Algorithmus (DE-588)4001183-5 gnd |
| topic_facet | Optimierung Algorithmus |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3423717&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=020811917&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bartzbeielsteinthomas experimentalmethodsfortheanalysisofoptimizationalgorithms |