Natural computing algorithms:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2015
|
| Schriftenreihe: | Natural computing series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XX, 554 S. 235 mm x 155 mm |
| ISBN: | 3662436302 9783662436301 |
Internformat
MARC
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| 020 | |a 9783662436301 |c Gb. : ca. EUR 53.49 (DE) (freier Pr.), ca. EUR 54.99 (AT) (freier Pr.), ca. sfr 67.00 (freier Pr.) |9 978-3-662-43630-1 | ||
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| 100 | 1 | |a Brabazon, Anthony |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Natural computing algorithms |c Anthony Brabazon ; Michael O'Neill ; Seán McGarraghy |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2015 | |
| 300 | |a XX, 554 S. |c 235 mm x 155 mm | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 0 | |a Natural computing series | |
| 650 | 0 | 7 | |a Natural Computing |0 (DE-588)1056970219 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Natural Computing |0 (DE-588)1056970219 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a O'Neill, Michael |e Verfasser |4 aut | |
| 700 | 1 | |a McGarraghy, Seán |e Verfasser |0 (DE-588)138030170 |4 aut | |
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Datensatz im Suchindex
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CONTENTS
1 INTRODUCTION 1
1.1 NATURAL COMPUTING ALGORITHMS: AN OVERVIEW 2
1.1.1 BIOLOGICALLY INSPIRED ALGORITHMS 2
1.1.2 FAMILIES OF NATURALLY INSPIRED ALGORITHMS 9
1.1.3 PHYSICALLY INSPIRED ALGORITHMS 10
1.1.4 PLANT INSPIRED ALGORITHMS 11
1.1.5 CHEMICALLY INSPIRED ALGORITHMS 11
1.1.6 A UNIFIED FAMILY OF ALGORITHMS 11
1.1.7 HOW MUCH NATURAL INSPIRATION? 12
1.2 STRUCTURE OF THE BOOK 12
PART I EVOLUTIONARY COMPUTING
2 INTRODUCTION TO EVOLUTIONARY COMPUTING 17
2.1 EVOLUTIONARY ALGORITHMS 18
3 GENETIC ALGORITHM 21
3.1 CANONICAL GENETIC ALGORITHM 21
3.1.1 A SIMPLE GA EXAMPLE 23
3.2 DESIGN CHOICES IN IMPLEMENTING A GA 24
3.3 CHOOSING A REPRESENTATION 25
3.3.1 GENOTYPE TO PHENOTYPE MAPPING 26
3.3.2 GENOTYPE ENCODINGS 26
3.3.3 REPRESENTATION CHOICE AND THE GENERATION OF DIVERSITY . 28
3.4 INITIALISING THE POPULATION 29
3.5 MEASURING FITNESS 29
3.6 GENERATING DIVERSITY 31
3.6.1 SELECTION STRATEGY 31
3.6.2 MUTATION AND CROSSOVER 35
3.6.3 REPLACEMENT STRATEGY 39
XI
HTTP://D-NB.INFO/105030702X
XII CONTENTS
3.7 CHOOSING PARAMETER VALUES 40
3.8 SUMMARY 41
4 EXTENDING THE GENETIC ALGORITHM 43
4.1 DYNAMIC ENVIRONMENTS 43
4.1.1 STRATEGIES FOR DYNAMIC ENVIRONMENTS 44
4.1.2 DIVERSITY 44
4.2 STRUCTURED POPULATION GAS 48
4.3 CONSTRAINED OPTIMISATION 50
4.4 MULTIOBJECTIVE OPTIMISATION 53
4.5 MEMETIC ALGORITHMS 57
4.6 LINKAGE LEARNING 59
4.7 ESTIMATION OF DISTRIBUTION ALGORITHMS 61
4.7.1 POPULATION-BASED INCREMENTAL LEARNING 62
4.7.2 UNIVARIATE MARGINAL DISTRIBUTION ALGORITHM 63
4.7.3 COMPACT GENETIC ALGORITHM 65
4.7.4 BAYESIAN OPTIMISATION ALGORITHM 66
4.8 SUMMARY 71
5 EVOLUTION STRATEGIES AND EVOLUTIONARY PROGRAMMING 73
5.1 THE CANONICAL ES ALGORITHM 74
5.1.1 (1 + 1)-ES 74
5.1.2 (FI
+ A)-ES AND {FX.
A)-ES 75
5.1.3 MUTATION IN ES 75
*5.1.4 ADAPTATION OF THE STRATEGY PARAMETERS 76
5.1.5 RECOMBINATION 78
5.2 EVOLUTIONARY PROGRAMMING 80
5.3 SUMMARY 82
6 DIFFERENTIAL EVOLUTION 83
6.1 CANONICAL DIFFERENTIAL EVOLUTION ALGORITHM 83
6.2 EXTENDING THE CANONICAL DE ALGORITHM 88
6.2.1 SELECTION OF THE BASE VECTOR 88
6.2.2 NUMBER OF VECTOR DIFFERENCES 88
6.2.3 ALTERNATIVE CROSSOVER RULES 89
6.2.4 OTHER DE VARIANTS 89
6.3 DISCRETE DE 90
6.4 SUMMARY 92
7 GENETIC PROGRAMMING 95
7.1 GENETIC PROGRAMMING 95
7.1.1 GP ALGORITHM 97
7.1.2 FUNCTION AND TERMINAL SETS 98
7.1.3 INITIALISATION STRATEGY 100
7.1.4 DIVERSITY-GENERATION IN GP 102
CONTENTS XIII
7.2 BLOAT IN GP 105
7.3 MORE COMPLEX GP ARCHITECTURES 105
7.3.1 FUNCTIONS 105
7.3.2 ADF MUTATION AND CROSSOVER 108
7.3.3 MEMORY 108
7.3.4 LOOPING 109
7.3.5 RECURSION ILL
7.4 GP VARIANTS 112
7.4.1 LINEAR AND GRAPH GP 112
7.4.2 STRONGLY TYPED GP 112
7.4.3 GRAMMAR-BASED GP 112
7.5 SEMANTICS AND GP 113
7.6 SUMMARY 113
PART II SOCIAL COMPUTING
8 PARTICLE SWARM ALGORITHMS 117
8.1 SOCIAL SEARCH 118
8.2 PARTICLE SWARM OPTIMISATION ALGORITHM 118
8.2.1 VELOCITY UPDATE 120
8.2.2 VELOCITY CONTROL 123
8.2.3 NEIGHBOURHOOD STRUCTURE 124
8.3 COMPARING PSO AND EVOLUTIONARY ALGORITHMS 125
8.4 MAINTAINING DIVERSITY IN PSO 127
8.4.1 SIMPLE APPROACHES TO MAINTAINING DIVERSITY 129
8.4.2 PREDATOR-PREY PSO 130
8.4.3 CHARGED PARTICLE SWARM 132
8.4.4 MULTIPLE SWARMS 134
8.4.5 SPECIATION-BASED PSO 135
8.5 HYBRID PSO ALGORITHMS 136
8.6 DISCRETE PSO 137
8.6.1 BINPSO 137
8.6.2 ANGLE-MODULATED PSO 138
8.7 EVOLVING A PSO ALGORITHM 139
8.8 SUMMARY 139
9 ANT ALGORITHMS 141
9.1 A TAXONOMY OF ANT ALGORITHMS 142
9.2 ANT FORAGING BEHAVIOURS 142
9.3 ANT ALGORITHMS FOR DISCRETE OPTIMISATION 144
9.3.1 GRAPH STRUCTURE 144
9.3.2 ANT SYSTEM 147
9.3.3 MAX-MXM ANT SYSTEM 151
9.3.4 ANT COLONY SYSTEM 152
XIV CONTENTS
9.3.5 ANT MULTITOUR SYSTEMS 153
9.3.G DYNAMIC OPTIMISATION 154
9.4 ANT ALGORITHMS FOR CONTINUOUS OPTIMISATION 155
9.5 MULTIPLE ANT COLONIES 157
9.6 HYBRID ANT FORAGING ALGORITHMS 159
9.7 ANT-INSPIRED CLUSTERING ALGORITHMS 160
9.7.1 DENEUBOURG MODEL 161
9.7.2 LUNIER AND FAIETA MODEL 162
9.7.3 CRITIQUING ANT CLUSTERING 166
9.8 CLASSIFICATION WITH ANT ALGORITHMS 167
9.9 EVOLVING AN ANT ALGORITHM 169
9.10 SUMMARY 170
10 OTHER FORAGING ALGORITHMS 171
10.1 HONEYBEE DANCE LANGUAGE 171
10.2 HONEYBEE FORAGING 172
10.2.1 THE HONEYBEE RECRUITMENT DANCE 172
10.3 DESIGNING A HONEYBEE FORAGING OPTIMISATION ALGORITHM 173
10.3.1 BEE SYSTEM ALGORITHM 174
10.3.2 ARTIFICIAL BEE COLONY ALGORITHM 175
10.3.3 HONEYBEE FORAGING AND DYNAMIC ENVIRONMENTS 178
10.4 BEE NEST SITE SELECTION 180
10.4.1 BEE NEST SITE SELECTION OPTIMISATION ALGORITHM 182
10.5 HONEYBEE MATING OPTIMISATION ALGORITHM 184
10.6 SUMMARY 186
11 BACTERIAL FORAGING ALGORITHMS 187
11.1 BACTERIAL BEHAVIOURS 187
11.1.1 QUORUM SENSING 187
11.1.2 SPORULATION 188
11.1.3 MOBILITY 188
11.2 CHEMOTAXIS IN E. COLI BACTERIA 189
11.3 BACTERIAL FORAGING OPTIMISATION ALGORITHM 190
11.3.1 BASIC CHEMOTAXIS MODEL 191
11.3.2 CHEMOTAXIS MODEL WITH SOCIAL COMMUNICATION 192
11.4 DYNAMIC ENVIRONMENTS 198
11.5 CLASSIFICATION USING A BACTERIAL FORAGING METAPHOR 198
11.6 SUMMARY 199
12 OTHER SOCIAL ALGORITHMS 201
12.1 GLOW WORM ALGORITHM 201
12.2 BAT ALGORITHM 206
12.2.1 BAT VOCALISATIONS 206
12.2.2 ALGORITHM 207
12.2.3 DISCUSSION 210
CONTENTS XV
12.3 FISH SCHOOL ALGORITHM . . J
;
12.3.1 FISH SCHOOL SEARCH _* I :
12.3.2 SUMMARY 214
12.4 LOCUSTS 215
12.4.1 LOCUST SWARM ALGORITHM 216
12.5 SUMMARY 218
PART III NEUROCOMPUTING
13 NEURAL NETWORKS FOR SUPERVISED LEARNING 221
13.1 BIOLOGICAL INSPIRATION FOR NEURAL NETWORKS 221
13.2 ARTIFICIAL NEURAL NETWORKS 222
13.2.1 NEURAL NETWORK ARCHITECTURES 222
13.3 STRUCTURE OF SUPERVISED NEURAL NETWORKS 224
13.3.1 ACTIVATION AND TRANSFER FUNCTIONS 226
13.3.2 UNIVERSAL APPROXIMATORS 228
13.4 THE MULTILAYER PERCEPTRON 228
13.4.1 MLP TRANSFER FUNCTION 230
13.4.2 MLP ACTIVATION FUNCTION 230
13.4.3 THE MLP PROJECTION CONSTRUCTION AND RESPONSE
REGIONS 231
13.4.4 RELATIONSHIP OF MLPS TO REGRESSION MODELS 233
13.4.5 TRAINING AN MLP 234
13.4.6 OVERTRAINING 237
13.4.7 PRACTICAL ISSUES IN MODELLING WITH AND TRAINING MLPS . 239
13.4.8 STACKING MLPS 243
13.4.9 RECURRENT NETWORKS 244
13.5 RADIAL BASIS FUNCTION NETWORKS 246
13.5.1 KERNEL FUNCTIONS 246
13.5.2 RADIAL BASIS FUNCTIONS 247
13.5.3 INTUITION BEHIND RADIAL BASIS FUNCTION NETWORKS 248
13.5.4 PROPERTIES OF RADIAL BASIS FUNCTION NETWORKS 249
13.5.5 TRAINING RADIAL BASIS FUNCTION NETWORKS 250
13.5.6 DEVELOPING A RADIAL BASIS FUNCTION NETWORK 251
13.6 SUPPORT VECTOR MACHINES 252
13.6.1 SVM METHOD 258
13.6.2 ISSUES IN APPLICATIONS OF SVM 258
13.7 SUMMARY 259
14 NEURAL NETWORKS FOR UNSUPERVISED LEARNING 261
14.1 SELF-ORGANISING MAPS 262
14.2 SOM ALGORITHM 264
14.3 IMPLEMENTING A SOM ALGORITHM 266
14.4 CLASSIFICATION WITH SOMS 271
XVI CONTENTS
14.5 SELF-ORGANISING SWARM 272
14.6 SOSWARM AND SOM 275
14.7 ADAPTIVE RESONANCE THEORY 276
14.7.1 UNSUPERVISED LEARNING FOR ART 277
14.7.2 SUPERVISED LEARNING FOR ARTS 279
14.7.3 WEAKNESSES OF ART APPROACHES 279
14.8 SUMMARY 279
15 NEUROEVOLUTION 281
15.1 DIRECT ENCODINGS 282
15.1.1 EVOLVING WEIGHT VECTORS 282
15.1.2 EVOLVING THE SELECTION OF INPUTS 283
15.1.3 EVOLVING THE CONNECTION STRUCTURE 283
15.1.4 OTHER HYBRID MLP ALGORITHMS 286
15.1.5 PROBLEMS WITH DIRECT ENCODINGS 287
15.2 NEAT 289
15.2.1 REPRESENTATION IN NEAT 290
15.2.2 DIVERSITY GENERATION IN NEAT 291
15.2.3 SPECIATION 292
15.2.4 INCREMENTAL EVOLUTION 296
15.3 INDIRECT ENCODINGS 297
15.4 OTHER HYBRID NEURAL ALGORITHMS 297
15.5 SUMMARY 297
PART IV IMMUNOCOMPUTING
16 ARTIFICIAL IMMUNE SYSTEMS 301
16.1 THE NATURAL IMMUNE SYSTEM 302
16.1.1 COMPONENTS OF THE NATURAL IMMUNE SYSTEM 302
16.1.2 INNATE IMMUNE SYSTEM 302
16.1.3 ADAPTIVE IMMUNE SYSTEM 304
16.1.4 DANGER THEORY 309
16.1.5 IMMUNE NETWORK THEORY 309
16.1.6 OPTIMAL IMMUNE DEFENCE 310
16.2 ARTIFICIAL IMMUNE ALGORITHMS 310
16.3 NEGATIVE SELECTION ALGORITHM 310
16.4 DENDRITRIC CELL ALGORITHM 315
16.5 CLONAL EXPANSION AND SELECTION INSPIRED ALGORITHMS 320
16.5.1 CLONALG ALGORITHM 320
16.5.2 B CELL ALGORITHM 322
16.5.3 REAL-VALUED CLONAL SELECTION ALGORITHM 323
16.5.4 ARTIFICIAL IMMUNE RECOGNITION SYSTEM 325
16.6 IMMUNE PROGRAMMING 330
16.7 SUMMARY 331
CONTENTS XVII
PART V DEVELOPMENTAL AND GRAMMATICAL COMPUTING
17 AN INTRODUCTION TO DEVELOPMENTAL AND GRAMMATICAL
COMPUTING 335
17.1 DEVELOPMENTAL COMPUTING 335
17.2 GRAMMATICAL COMPUTING 336
17.3 WHAT IS A GRAMMAR? 337
17.3.1 TYPES OF GRAMMAR 338
17.3.2 FORMAL GRAMMAR NOTATION 340
17.4 GRAMMATICAL INFERENCE 341
17.5 LINDENMAYER SYSTEMS 341
17.6 SUMMARY 343
18 GRAMMAR-BASED AND DEVELOPMENTAL GENETIC
PROGRAMMING 345
18.1 GRAMMAR-GUIDED GENETIC PROGRAMMING 346
18.1.1 OTHER GRAMMAR-BASED APPROACHES TO GP 351
18.2 DEVELOPMENTAL GP 351
18.2.1 GENETIC L-SYSTEM PROGRAMMING 351
18.2.2 BINARY GP 352
18.2.3 CELLULAR ENCODING 354
18.2.4 ANALOG CIRCUITS 354
18.2.5 OTHER DEVELOPMENTAL APPROACHES TO GP 354
18.3 SUMMARY 356
19 GRAMMATICAL EVOLUTION 357
19.1 A PRIMER ON GENE EXPRESSION 358
19.2 EXTENDING THE BIOLOGICAL ANALOGY TO GE 360
19.3 EXAMPLE GE MAPPING 361
19.4 SEARCH ENGINE 368
19.4.1 GENOME ENCODING 368
19.4.2 MUTATION AND CROSSOVER SEARCH OPERATORS 368
19.4.3 MODULARITY 370
19.4.4 SEARCH ALGORITHM 371
19.5 GENOTYPE-PHENOTYPE MAP 372
19.6 GRAMMARS 372
19.7 SUMMARY 373
20 TREE-ADJOINING GRAMMARS AND GENETIC PROGRAMMING 375
20.1 TREE-ADJOINING GRAMMARS 377
20.2 TAG3P 377
20.3 DEVELOPMENTAL TAG3P 379
20.4 TAGE 379
20.5 SUMMARY 381
XVIII CONTENTS
21 GENETIC REGULATORY NETWORKS 383
21.1 ARTIFICIAL GENE REGULATORY MODEL FOR GENETIC PROGRAMMING. 383
21.1.1 MODEL OUTPUT 385
21.2 DIFFERENTIAL GENE EXPRESSION 386
21.3 ARTIFICIAL GRN FOR IMAGE COMPRESSION 389
21.4 SUMMARY 389
PART VI PHYSICAL COMPUTING
22 AN INTRODUCTION TO PHYSICALLY INSPIRED COMPUTING 393
22.1 A BRIEF PHYSICS PRIMER 393
22.1.1 A ROUGH TAXONOMY OF MODERN PHYSICS 393
22.2 CLASSICAL MECHANICS 395
22.2.1 ENERGY AND MOMENTUM 395
22.2.2 THE HAMILTONIAN 396
22.3 THERMODYNAMICS 398
22.3.1 STATISTICAL MECHANICS 400
22.3.2 ERGODICITY 402
22.4 QUANTUM MECHANICS 402
22.4.1 OBSERVATION IN QUANTUM MECHANICS 403
22.4.2 ENTANGLEMENT AND DECOHERENCE 404
22.4.3 NONCOMMUTING OPERATORS 405
22.4.4 TUNNELLING 406
22.4.5 QUANTUM STATISTICAL MECHANICS 406
22.5 QUANTUM COMPUTING 407
22.5.1 TWO-STATE SYSTEMS AND QUBITS 407
22.5.2 DIGITAL QUANTUM COMPUTERS 408
22.5.3 QUANTUM INFORMATION 409
22.5.4 ADIABATIC QUANTUM COMPUTATION 410
22.6 ANNEALING AND SPIN GLASSES 411
22.6.1 ISING SPIN GLASSES 412
22.6.2 QUANTUM SPIN GLASSES 414
22.7 SUMMARY 415
23 PHYSICALLY INSPIRED COMPUTING ALGORITHMS 417
23.1 SIMULATED ANNEALING 417
23.1.1 SEARCH AND NEIGHBOURHOODS 419
23.1.2 ACCEPTANCE OF 'BAD' MOVES 419
23.1.3 PARAMETERISATION OF SA 420
23.1.4 EXTENSIONS OF SA 421
23.1.5 CONCLUDING REMARKS 421
23.2 SIMULATED QUANTUM ANNEALING 422
23.2.1 IMPLEMENTATION OF SQA 424
23.2.2 SQA APPLICATION TO TSP-TYPE PROBLEMS 424
CONTENTS XIX
23.3 CONSTRAINED MOLECULAR DYNAMICS ALGORITHM 426
23.4 PHYSICAL FIELD INSPIRED ALGORITHMS 429
23.4.1 CENTRAL FORCE OPTIMISATION 429
23.4.2 GRAVITATIONAL SEARCH ALGORITHM AND VARIANTS 431
23.4.3 DIFFERENCES AMONG PHYSICAL FIELD-INSPIRED ALGORITHMS . 435
23.5 EXTREMAL OPTIMISATION ALGORITHM 436
23.6 SUMMARY 437
24 QUANTUM INSPIRED EVOLUTIONARY ALGORITHMS 439
24.1 QUBIT, REPRESENTATION 439
24.2 QUANTUM INSPIRED EVOLUTIONARY ALGORITHMS (QIEAS) 440
24.3 BINARY-VALUED QIEA 440
24.3.1 DIVERSITY GENERATION IN BINARY QIEA 443
24.4 REAL-VALUED QIEA 446
24.4.1 INITIALISING THE QUANTUM POPULATION 446
24.4.2 OBSERVING THE QUANTUM CHROMOSOMES 447
24.4.3 CROSSOVER MECHANISM 448
24.4.4 UPDATING THE QUANTUM CHROMOSOMES 449
24.5 QIEAS AND EDAS 449
24.6 OTHER QUANTUM HYBRID ALGORITHMS 450
24.7 SUMMARY 452
PART VII OTHER PARADIGMS
25 PLANT-INSPIRED ALGORITHMS 455
25.1 PLANT BEHAVIOURS 455
25.2 FORAGING 456
25.2.1 PLANT MOVEMENT AND FORAGING 457
25.2.2 ROOT FORAGING 459
25.2.3 PREDATORY PLANTS 461
25.3 PLANT-LEVEL COORDINATION 462
25.4 A TAXONOMY OF PLANT-INSPIRED ALGORITHMS 464
25.5 PLANT PROPAGATION ALGORITHMS 464
25.5.1 INVASIVE WEED OPTIMISATION ALGORITHM 464
25.5.2 PADDY FIELD ALGORITHM 467
25.5.3 STRAWBERRY PLANT ALGORITHM 468
25.6 PLANT GROWTH SIMULATION ALGORITHM 469
25.6.1 THE ALGORITHM 472
25.6.2 VARIANTS ON THE PLANT GROWTH SIMULATION ALGORITHM . 474
25.7 ROOT,-SWARM BEHAVIOUR 475
25.7.1 MODELLING ROOT GROWTH IN REAL PLANTS 476
25.7.2 APPLYING THE ROOT-SWARM METAPHOR FOR OPTIMISATION. 476
25.8 SUMMARY 477
XX CONTENTS
26 CHEMICALLY INSPIRED ALGORITHMS 479
26.1 A BRIEF CHEMISTRY PRIMER 479
26.2 CHEMICALLY INSPIRED ALGORITHMS 481
26.2.1 CHEMICAL REACTION OPTIMISATION (CRO) 481
26.2.2 ARTIFICIAL CHEMICAL REACTION OPTIMISATION ALGORITHM
(ACROA) 482
26.3 THE CR.0 ALGORITHM 483
26.3.1 POTENTIAL AND KINETIC ENERGY AND THE BUFFER 483
26.3.2 TYPES OF COLLISION AND REACTION 484
26.3.3 THE HIGH-LEVEL CRO ALGORITHM 485
26.3.4 ON-WALL INEFFECTIVE COLLISION 489
26.3.5 DECOMPOSITION 489
26.3.6 INTERMOLECULAR INEFFECTIVE COLLISION 490
26.3.7 SYNTHESIS 492
26.4 APPLICATIONS OF CRO 493
26.5 DISCUSSION OF CRO 494
26.5.1 POTENTIAL FUTURE AVENUES FOR RESEARCH 496
26.6 SUMMARY 498
PART VIII THE FUTURE OF NATURAL COMPUTING ALGORITHMS
27 LOOKING AHEAD 501
27.1 OPEN ISSUES 501
27.1.1 HYBRID ALGORITHMS 501
27.1.2 THE POWER AND THE DANGERS OF METAPHOR 502
27.1.3 BENCHMARKS AND SCALABILITY 502
27.1.4 USABILITY AND PARAMETER-FREE ALGORITHMS 503
27.1.5 SIMULATION AND KNOWLEDGE DISCOVERY 503
27.2 CONCLUDING REMARKS 503
REFERENCES 505
INDEX 547 |
| any_adam_object | 1 |
| author | Brabazon, Anthony O'Neill, Michael McGarraghy, Seán |
| author_GND | (DE-588)138030170 |
| author_facet | Brabazon, Anthony O'Neill, Michael McGarraghy, Seán |
| author_role | aut aut aut |
| author_sort | Brabazon, Anthony |
| author_variant | a b ab m o mo s m sm |
| building | Verbundindex |
| bvnumber | BV043049125 |
| classification_rvk | ST 134 ST 301 |
| ctrlnum | (OCoLC)878982175 (DE-599)DNB105030702X |
| dewey-full | 004 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science |
| dewey-raw | 004 |
| dewey-search | 004 |
| dewey-sort | 14 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV043049125 |
| illustrated | Not Illustrated |
| indexdate | 2024-09-10T01:57:11Z |
| institution | BVB |
| isbn | 3662436302 9783662436301 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028473548 |
| oclc_num | 878982175 |
| open_access_boolean | |
| owner | DE-11 DE-92 |
| owner_facet | DE-11 DE-92 |
| physical | XX, 554 S. 235 mm x 155 mm |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Springer |
| record_format | marc |
| series2 | Natural computing series |
| spelling | Brabazon, Anthony Verfasser aut Natural computing algorithms Anthony Brabazon ; Michael O'Neill ; Seán McGarraghy Berlin [u.a.] Springer 2015 XX, 554 S. 235 mm x 155 mm txt rdacontent n rdamedia nc rdacarrier Natural computing series Natural Computing (DE-588)1056970219 gnd rswk-swf Natural Computing (DE-588)1056970219 s DE-604 O'Neill, Michael Verfasser aut McGarraghy, Seán Verfasser (DE-588)138030170 aut Erscheint auch als Online-Ausgabe 978-3-662-43631-8 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4648252&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=028473548&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Brabazon, Anthony O'Neill, Michael McGarraghy, Seán Natural computing algorithms Natural Computing (DE-588)1056970219 gnd |
| subject_GND | (DE-588)1056970219 |
| title | Natural computing algorithms |
| title_auth | Natural computing algorithms |
| title_exact_search | Natural computing algorithms |
| title_full | Natural computing algorithms Anthony Brabazon ; Michael O'Neill ; Seán McGarraghy |
| title_fullStr | Natural computing algorithms Anthony Brabazon ; Michael O'Neill ; Seán McGarraghy |
| title_full_unstemmed | Natural computing algorithms Anthony Brabazon ; Michael O'Neill ; Seán McGarraghy |
| title_short | Natural computing algorithms |
| title_sort | natural computing algorithms |
| topic | Natural Computing (DE-588)1056970219 gnd |
| topic_facet | Natural Computing |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4648252&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=028473548&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT brabazonanthony naturalcomputingalgorithms AT oneillmichael naturalcomputingalgorithms AT mcgarraghysean naturalcomputingalgorithms |