How to solve it: modern heuristics:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2004
|
| Ausgabe: | 2., rev. and extended ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XVIII, 554 S. graph. Darst. |
| ISBN: | 9783642061349 3540224947 |
Internformat
MARC
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| 100 | 1 | |a Michalewicz, Zbigniew |d 1952- |e Verfasser |0 (DE-588)12115419X |4 aut | |
| 245 | 1 | 0 | |a How to solve it: modern heuristics |c Zbigniew Michalewicz ; David B. Fogel |
| 250 | |a 2., rev. and extended ed. | ||
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2004 | |
| 300 | |a XVIII, 554 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 650 | 7 | |a Algoritmen |2 gtt | |
| 650 | 7 | |a Heuristiek |2 gtt | |
| 650 | 4 | |a Heuristique | |
| 650 | 4 | |a Jeux mathématiques | |
| 650 | 4 | |a Matematiksel eğlenceler | |
| 650 | 7 | |a Mathematische programmering |2 gtt | |
| 650 | 7 | |a Probleemoplossing |2 gtt | |
| 650 | 4 | |a Résolution de problème | |
| 650 | 4 | |a Sezgisel | |
| 650 | 4 | |a Sorun çözme | |
| 650 | 4 | |a Heuristic | |
| 650 | 4 | |a Mathematical recreations | |
| 650 | 4 | |a Problem solving | |
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Datensatz im Suchindex
| DE-BY-862_location | 2000 |
|---|---|
| DE-BY-FWS_call_number | 2000/QH 400 M621(2) |
| DE-BY-FWS_katkey | 284265 |
| DE-BY-FWS_media_number | 083000522926 |
| _version_ | 1806174615550033920 |
| adam_text | Table of Contents
Introduction 1
I What Are the Ages of My Three Sons? 9
1 Why Are Some Problems Difficult to Solve? 11
1.1 The size of the search space 11
1.2 Modeling the problem 16
1.3 Change over time 19
1.4 Constraints 21
1.5 The problem of proving things 24
1.6 Your chance for glory 26
1.7 Summary 29
II How Important Is a Model? 31
2 Basic Concepts 35
2.1 Representation 35
2.2 The objective 36
2.3 The evaluation function 37
2.4 Defining a search problem 39
2.5 Neighborhoods and local optima 40
2.6 Hill climbing methods 43
2.7 Can you sink this trick shot? 45
2.8 Summary 48
III What Are the Prices in 7 11? 49
3 Traditional Methods — Part 1 55
3.1 Exhaustive search 58
3.1.1 Enumerating the SAT 59
3.1.2 Enumerating the TSP 61
3.1.3 Enumerating the NLP 62
3.2 Local search 64
3.2.1 Local search and the SAT 65
3.2.2 Local search and the TSP 66
XIV Table of Contents
3.2.3 Local search and the NLP 69
3.3 Linear programming: The simplex method 76
3.4 Summary 80
IV What Are the Numbers? 83
4 Traditional Methods — Part 2 87
4.1 Greedy algorithms 87
4.1.1 Greedy algorithms and the SAT 87
4.1.2 Greedy algorithms and the TSP 89
4.1.3 Greedy algorithms and the NLP 90
4.2 Divide and conquer 90
4.3 Dynamic programming 93
4.4 Branch and bound 101
4.5 A* algorithm 105
4.6 Summary 109
V What s the Color of the Bear? Ill
5 Escaping Local Optima 115
5.1 Simulated annealing 117
5.2 Tabu search 125
5.3 Summary 134
VI How Good Is Your Intuition? 135
6 An Evolutionary Approach 139
6.1 Evolutionary approach for the SAT 142
6.2 Evolutionary approach for the TSP 145
6.3 Evolutionary approach for the NLP 149
6.4 Summary 151
VII One of These Things Is Not Like the Others 157
7 Designing Evolutionary Algorithms 161
7.1 Representation 165
7.1.1 Fixed length vectors of symbols 166
7.1.2 Permutations 167
7.1.3 Finite state machines 168
7.1.4 Symbolic expressions 168
7.2 Evaluation function 169
7.3 Variation operators 172
7.3.1 Fixed length vectors of symbols 173
7.3.2 Permutations 174
7.3.3 Finite state machines 176
7.3.4 Symbolic expressions 177
7.4 Selection 179
Table of Contents XV
7.5 Initialization 181
7.6 Summary 183
VIII What Is the Shortest Way? 185
8 The Traveling Salesman Problem 189
8.1 In search of good variation operators 191
8.2 Incorporating local search methods 214
8.3 Other possibilities 217
8.3.1 Edge assembly crossover 217
8.3.2 Inver over operator 220
8.4 Summary 223
IX Who Owns the Zebra? 225
9 Constraint Handling Techniques 231
9.1 General considerations 232
9.1.1 Designing evalf 234
9.1.2 Design of evalu 236
9.1.3 Relationship between evalf and evalu 237
9.1.4 Rejecting infeasible solutions 239
9.1.5 Repairing infeasible individuals 239
9.1.6 Replacing individuals by their repaired versions 240
9.1.7 Penalizing infeasible individuals 240
9.1.8 Maintaining a feasible population using special represen¬
tations and variation operators 241
9.1.9 Using decoders 242
9.1.10 Separating individuals and constraints 243
9.1.11 Exploring boundaries between feasible and infeasible parts
of the search space 243
9.1.12 Finding feasible solutions 244
9.2 Numerical optimization 245
9.2.1 Methods based on preserving the feasibility of solutions . 246
9.2.2 Methods based on penalty functions 249
9.2.3 Methods based on a search for feasible solutions 257
9.2.4 Methods based on decoders 264
9.2.5 Hybrid methods 266
9.3 Summary 268
X Can You Tune to the Problem? 271
10 Tuning the Algorithm to the Problem 277
10.1 Parameter control in evolutionary algorithms 277
10.2 Illustrating the case with an NLP 282
10.3 Taxonomy of control techniques 284
10.4 The possibilities for parameter control 288
XVI Table of Contents
10.4.1 Representation 288
10.4.2 Evaluation function 289
10.4.3 Mutation operators and their probabilities 290
10.4.4 Crossover operators and their probabilities 293
10.4.5 Parent selection 295
10.4.6 Population 295
10.5 Combining forms of parameter control 296
10.6 Summary 298
XI Can You Mate in Two Moves? 303
11 Time Varying Environments and Noise 307
11.1 Life presents a dynamic landscape 307
11.2 The real world is noisy 317
11.2.1 Ensuring diversity 321
11.3 Modeling of ship trajectory 325
11.4 Summary 329
XII Day of the Week of January 1st 335
12 Neural Networks 339
12.1 Threshold neurons and linear discriminant functions 340
12.2 Back propagation for feed forward multilayer perceptrons .... 345
12.3 Training and testing 350
12.4 Recurrent networks and extended architectures 352
12.4.1 Standard recurrent network 352
12.4.2 Hopfield network 352
12.4.3 Boltzmann machine 354
12.4.4 Network of multiple interacting programs 354
12.5 Clustering with competitive networks 356
12.6 Using neural networks to solve the TSP 358
12.7 Evolving neural networks 360
12.8 Summary 361
XIII What Wras the Length of the Rope? 363
13 Fuzzy Systems 367
13.1 Fuzzy sets 368
13.2 Fuzzy sets and probability measures 368
13.3 Fuzzy set operations 369
13.4 Fuzzy relationships 372
13.5 Designing a fuzzy controller 375
13.6 Fuzzy clustering 380
13.7 Fuzzy neural networks 382
13.8 A fuzzy TSP 385
13.9 Evolving fuzzy systems 387
Table of Contents XVII
13.10 Summary 388
XIV Everything Depends on Something Else 389
14 Coevolutionary Systems 397
14.1 Playing a game 399
14.2 Learning to play checkers 400
14.3 Optimization through competing populations 403
14.4 Another example with a game 408
14.5 Artificial life 411
14.6 Games of cooperation and competition 412
14.7 Modeling intelligent agents 418
14.8 Issues in coevolution 420
14.9 An example of cooperative coevolution on the TSP 424
14.10 Summary 430
XV Who s Taller? 431
15 Multicriteria Decision Making 435
15.1 Single numeric approach 440
15.1.1 The Valuated State Space approach 443
15.1.2 Fuzzy multicriteria decision making 449
15.1.3 Summary 450
15.2 Evolutionary multiobjective optimization approach 451
15.2.1 Brief history 452
15.2.2 Evolutionary algorithm for multiobjective NLP 458
15.3 Summary 462
XVI Do You Like Simple Solutions? 465
16 Hybrid Systems 471
16.1 Summary 480
17 Summary 483
Appendix A: Probability and Statistics 495
A.I Basic concepts of probability 495
A.2 Random variables 497
A.2.1 Discrete random variables 497
A.2.2 Continuous random variables 500
A.3 Descriptive statistics of random variables 502
A.4 Limit theorems and inequalities 505
A.5 Adding random variables 506
A.6 Generating random numbers on a computer 507
A.7 Estimation 508
A.8 Statistical hypothesis testing 511
A.9 Linear regression 512
XVIII Table of Contents
A.10 Summary 514
Appendix B: Problems and Projects 515
B.I Trying some practical problems 517
B.2 Reporting computational experiments with heuristic methods . . 522
References 525
Index 551
|
| any_adam_object | 1 |
| author | Michalewicz, Zbigniew 1952- Fogel, David B. 1964- |
| author_GND | (DE-588)12115419X (DE-588)121154203 |
| author_facet | Michalewicz, Zbigniew 1952- Fogel, David B. 1964- |
| author_role | aut aut |
| author_sort | Michalewicz, Zbigniew 1952- |
| author_variant | z m zm d b f db dbf |
| building | Verbundindex |
| bvnumber | BV019689515 |
| callnumber-first | Q - Science |
| callnumber-label | QA63 |
| callnumber-raw | QA63 |
| callnumber-search | QA63 |
| callnumber-sort | QA 263 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 400 QP 327 SK 110 ST 200 |
| classification_tum | MAT 010f |
| ctrlnum | (OCoLC)249628691 (DE-599)BVBBV019689515 |
| dewey-full | 153.42 |
| dewey-hundreds | 100 - Philosophy & psychology |
| dewey-ones | 153 - Conscious mental processes & intelligence |
| dewey-raw | 153.42 |
| dewey-search | 153.42 |
| dewey-sort | 3153.42 |
| dewey-tens | 150 - Psychology |
| discipline | Informatik Psychologie Mathematik Wirtschaftswissenschaften |
| edition | 2., rev. and extended ed. |
| format | Book |
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| id | DE-604.BV019689515 |
| illustrated | Illustrated |
| indexdate | 2024-08-01T10:52:21Z |
| institution | BVB |
| isbn | 9783642061349 3540224947 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-013017353 |
| oclc_num | 249628691 |
| open_access_boolean | |
| owner | DE-29 DE-M347 DE-384 DE-703 DE-29T DE-91G DE-BY-TUM DE-20 DE-83 DE-11 DE-188 DE-473 DE-BY-UBG DE-862 DE-BY-FWS |
| owner_facet | DE-29 DE-M347 DE-384 DE-703 DE-29T DE-91G DE-BY-TUM DE-20 DE-83 DE-11 DE-188 DE-473 DE-BY-UBG DE-862 DE-BY-FWS |
| physical | XVIII, 554 S. graph. Darst. |
| publishDate | 2004 |
| publishDateSearch | 2004 |
| publishDateSort | 2004 |
| publisher | Springer |
| record_format | marc |
| spellingShingle | Michalewicz, Zbigniew 1952- Fogel, David B. 1964- How to solve it: modern heuristics Algoritmen gtt Heuristiek gtt Heuristique Jeux mathématiques Matematiksel eğlenceler Mathematische programmering gtt Probleemoplossing gtt Résolution de problème Sezgisel Sorun çözme Heuristic Mathematical recreations Problem solving Operations Research (DE-588)4043586-6 gnd Problemlösen (DE-588)4076358-4 gnd Optimierung (DE-588)4043664-0 gnd Heuristik (DE-588)4024772-7 gnd |
| subject_GND | (DE-588)4043586-6 (DE-588)4076358-4 (DE-588)4043664-0 (DE-588)4024772-7 |
| title | How to solve it: modern heuristics |
| title_auth | How to solve it: modern heuristics |
| title_exact_search | How to solve it: modern heuristics |
| title_full | How to solve it: modern heuristics Zbigniew Michalewicz ; David B. Fogel |
| title_fullStr | How to solve it: modern heuristics Zbigniew Michalewicz ; David B. Fogel |
| title_full_unstemmed | How to solve it: modern heuristics Zbigniew Michalewicz ; David B. Fogel |
| title_short | How to solve it: modern heuristics |
| title_sort | how to solve it modern heuristics |
| topic | Algoritmen gtt Heuristiek gtt Heuristique Jeux mathématiques Matematiksel eğlenceler Mathematische programmering gtt Probleemoplossing gtt Résolution de problème Sezgisel Sorun çözme Heuristic Mathematical recreations Problem solving Operations Research (DE-588)4043586-6 gnd Problemlösen (DE-588)4076358-4 gnd Optimierung (DE-588)4043664-0 gnd Heuristik (DE-588)4024772-7 gnd |
| topic_facet | Algoritmen Heuristiek Heuristique Jeux mathématiques Matematiksel eğlenceler Mathematische programmering Probleemoplossing Résolution de problème Sezgisel Sorun çözme Heuristic Mathematical recreations Problem solving Operations Research Problemlösen Optimierung Heuristik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013017353&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT michalewiczzbigniew howtosolveitmodernheuristics AT fogeldavidb howtosolveitmodernheuristics |
Inhaltsverzeichnis
THWS Schweinfurt Zentralbibliothek Lesesaal
| Signatur: |
2000 QH 400 M621(2) |
|---|---|
| Exemplar 1 | ausleihbar Verfügbar Bestellen |