Evolutionary optimization algorithms: biologically-inspired and population-based approaches to computer intelligence
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken
Wiley
2013
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| Schlagworte: | |
| Online-Zugang: | Cover image Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXX, 742 S. Ill., graph. Darst. |
| ISBN: | 9780470937419 |
Internformat
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| 100 | 1 | |a Simon, Dan |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Evolutionary optimization algorithms |b biologically-inspired and population-based approaches to computer intelligence |c Dan Simon |
| 264 | 1 | |a Hoboken |b Wiley |c 2013 | |
| 300 | |a XXX, 742 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Evolutionary computation | |
| 650 | 4 | |a Computer algorithms | |
| 650 | 4 | |a Biologically-inspired computing | |
| 650 | 7 | |a MATHEMATICS / Discrete Mathematics |2 bisacsh | |
| 650 | 0 | 7 | |a Evolutionärer Algorithmus |0 (DE-588)4366912-8 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
| _version_ | 1804150308967809024 |
|---|---|
| adam_text | Titel: Evolutionary optimization algorithms
Autor: Simon, Dan
Jahr: 2013
DETAILED TABLE OF CONTENTS
Acknowledgments xxi
Acronyms xxiii
List of Algorithms xxvii
PART I INTRODUCTION TO EVOLUTIONARY OPTIMIZATION
1 Introduction 1
1.1 Terminology 2
1.2 Why Another Book on Evolutionary Algorithms? 4
1.3 Prerequisites 5
1.4 Homework Problems 5
1.5 Notation 6
1.6 Outline of the Book 7
1.7 A Course Based on This Book 8
2 Optimization 11
2.1 Unconstrained Optimization 12
2.2 Constrained Optimization 15
2.3 Multi-Objective Optimization 16
2.4 Multimodal Optimization 19
2.5 Combinatorial Optimization 20
2.6 Hill Climbing 21
2.6.1 Biased Optimization Algorithms 25
2.6.2 The Importance of Monte Carlo Simulations 26
2.7 Intelligence 26
2.7.1 Adaptation 26
2.7.2 Randomness 27
2.7.3 Communication 27
2.7.4 Feedback 28
2.7.5 Exploration and Exploitation 28
2.8 Conclusion 29
Problems 30
PART II CLASSIC EVOLUTIONARY ALGORITHMS
3 Genetic Algorithms 35
3.1 The History of Genetics 36
3.1.1 Charles Darwin 36
3.1.2 Gregor Mendel 38
3.2 The Science of Genetics 39
3.3 The History of Genetic Algorithms 41
3.4 A Simple Binary Genetic Algorithm 44
3.4.1 A Genetic Algorithm for Robot Design 44
3.4.2 Selection and Crossover 45
3.4.3 Mutation 49
3.4.4 GA Summary 49
3.4.5 GA Tuning Parameters and Examples 49
3.5 A Simple Continuous Genetic Algorithm 55
3.6 Conclusion 59
Problems 60
4 Mathematical Models of Genetic Algorithms 63
4.1 Schema Theory 64
4.2 Markov Chains 68
4.3 Markov Model Notation for Evolutionary Algorithms 73
4.4 Markov Models of Genetic Algorithms 76
4.4.1 Selection 76
4.4.2 Mutation 77
4.4.3 Crossover 78
4.5 Dynamic System Models of Genetic Algorithms 82
4.5.1 Selection 82
4.5.2 Mutation 85
4.5.3 Crossover 87
4.6 Conclusion 92
Problems 93
Evolutionary Programming 95
5.1 Continuous Evolutionary Programming 96
5.2 Finite State Machine Optimization 100
5.3 Discrete Evolutionary Programming 103
5.4 The Prisoner s Dilemma 105
5.5 The Artificial Ant Problem 109
5.6 Conclusion 113
Problems 114
Evolution Strategies 117
6.1 The (1+1) Evolution Strategy 118
6.2 The 1/5 Rule: A Derivation 122
6.3 The (µ+1) Evolution Strategy 125
6.4 (µ + A) and (µ, A) Evolution Strategies 128
6.5 Self-Adaptive Evolution Strategies 131
6.6 Conclusion 136
Problems 138
Genetic Programming 141
7.1 Lisp: The Language of Genetic Programming 143
7.2 The Fundamentals of Genetic Programming 148
7.2.1 Fitness Measure 149
7.2.2 Termination Criteria 149
7.2.3 Terminal Set 150
7.2.4 Function Set 150
7.2.5 Initialization 152
7.2.6 Genetic Programming Parameters 155
7.3 Genetic Programming for Minimum Time Control 158
7.4 Genetic Programming Bloat 163
7.5 Evolving Entities other than Computer Programs 164
7.6 Mathematical Analysis of Genetic Programming 167
7.6.1 Definitions and Notation 167
7.6.2 Selection and Crossover 168
7.6.3 Mutation and Final Results 172
7.7 Conclusion 173
Problems 175
Evolutionary Algorithm Variations 179
8.1 Initialization 180
8.2 Convergence Criteria 181
8.3 Problem Representation Using Gray Coding 183
8.4 Elitism 188
8.5 Steady-State and Generational Algorithms 190
8.6 Population Diversity 192
8.6.1 Duplicate Individuals 192
8.6.2 Niche-Based and Species-Based Recombination 193
8.6.3 Niching 194
8.7 Selection Options 199
8.7.1 Stochastic Universal Sampling 199
8.7.2 Over-Selection 201
8.7.3 Sigma Scaling 202
8.7.4 Rank-Based Selection 203
8.7.5 Linear Ranking 205
8.7.6 Tournament Selection 207
8.7.7 Stud Evolutionary Algorithms 207
8.8 Recombination 209
8.8.1 Single-Point Crossover (Binary or Continuous EAs) 209
8.8.2 Multiple-Point Crossover (Binary or Continuous EAs) 210
8.8.3 Segmented Crossover (Binary or Continuous EAs) 210
8.8.4 Uniform Crossover (Binary or Continuous EAs) 210
8.8.5 Multi-Parent Crossover (Binary or Continuous EAs) 211
8.8.6 Global Uniform Crossover (Binary or Continuous EAs) 211
8.8.7 Shuffle Crossover (Binary or Continuous EAs) 212
8.8.8 Flat Crossover and Arithmetic Crossover (Continuous EAs) 212
8.8.9 Blended Crossover (Continuous EAs) 213
8.8.10 Linear Crossover (Continuous EAs) 213
8.8.11 Simulated Binary Crossover (Continuous EAs) 213
8.8.12 Summary 214
8.9 Mutation 214
8.9.1 Uniform Mutation Centered at Xi(k) 214
8.9.2 Uniform Mutation Centered at the Middle of the Search
Domain 215
8.9.3 Gaussian Mutation Centered at Xi(k) 215
8.9.4 Gaussian Mutation Centered at the Middle of the Search
Domain 215
8.10 Conclusion 215
Problems 217
PART III MORE RECENT EVOLUTIONARY ALGORITHMS
9 Simulated Annealing 223
9.1 Annealing in Nature 224
9.2 A Simple Simulated Annealing Algorithm 225
9.3 Cooling Schedules 227
9.3.1 Linear Cooling 227
9.3.2 Exponential Cooling 228
9.3.3 Inverse Cooling 228
9.3.4 Logarithmic Cooling 230
9.3.5 Inverse Linear Cooling 232
9.3.6 Dimension-Dependent Cooling 234
9.4 Implementation Issues 237
9.4.1 Candidate Solution Generation 237
9.4.2 Reinitialization 237
9.4.3 Keeping Track of the Best Candidate Solution 237
9.5 Conclusion 238
Problems 239
10 Ant Colony Optimization 241
10.1 Pheromone Models 244
10.2 Ant System 246
10.3 Continuous Optimization 252
10.4 Other Ant Systems 255
10.4.1 Max-Min Ant System 255
10.4.2 Ant Colony System 257
10.4.3 Even More Ant Systems 260
10.5 Theoretical Results 261
10.6 Conclusion 262
Problems 263
11 Particle Swarm Optimization 265
11.1 A Basic Particle Swarm Optimization Algorithm 267
11.2 Velocity Limiting 270
11.3 Inertia Weighting and Constriction Coefficients 271
11.3.1 Inertia Weighting 271
11.3.2 The Constriction Coefficient 273
11.3.3 PSO Stability 275
11.4 Global Velocity Updates 279
11.5 The Fully Informed Particle Swarm 282
11.6 Learning from Mistakes 285
11.7 Conclusion 288
Problems 290
12 Differential Evolution 293
12.1 A Basic Differential Evolution Algorithm 294
12.2 Differential Evolution Variations 296
12.2.1 Trial Vectors 296
12.2.2 Mutant Vectors 298
12.2.3 Scale Factor Adjustment 302
12.3 Discrete Optimization 305
12.3.1 Mixed-Integer Differential Evolution 306
12.3.2 Discrete Differential Evolution 307
12.4 Differential Evolution and Genetic Algorithms 307
12.5 Conclusion 309
Problems 310
13 Estimation of Distribution Algorithms 313
13.1 Estimation of Distribution Algorithms: Basic Concepts 314
13.1.1 A Simple Estimation of Distribution Algorithm 314
13.1.2 Computations of Statistics 314
13.2 First-Order Estimation of Distribution Algorithms 315
13.2.1 The Univariate Marginal Distribution Algorithm (UMDA) 316
13.2.2 The Compact Genetic Algorithm (cGA) 318
13.2.3 Population Based Incremental Learning (PBIL) 321
13.3 Second-Order Estimation of Distribution Algorithms 324
13.3.1 Mutual Information Maximization for Input Clustering
(MIMIC) 324
13.3.2 Combining Optimizers with Mutual Information Trees
(COMIT) 329
13.3.3 The Bivariate Marginal Distribution Algorithm (BMDA) 335
13.4 Multivariate Estimation of Distribution Algorithms 337
13.4.1 The Extended Compact Genetic Algorithm (ECGA) 337
13.4.2 Other Multivariate Estimation of Distribution Algorithms 340
13.5 Continuous Estimation of Distribution Algorithms 341
13.5.1 The Continuous Univariate Marginal Distribution Algorithm 342
13.5.2 Continuous Population Based Incremental Learning 343
13.6 Conclusion 347
Problems 348
14 Biogeography-Based Optimization 351
14.1 Biogeography 352
14.2 Biogeography is an Optimization Process 357
14.3 Biogeography-Based Optimization 359
14.4 BBO Extensions 363
14.4.1 Migration Curves 363
14.4.2 Blended Migration 365
14.4.3 Other Approaches to BBO 366
14.4.4 BBO and Genetic Algorithms 369
14.5 Conclusion 370
Problems 374
15 Cultural Algorithms 377
15.1 Cooperation and Competition 378
15.2 Belief Spaces in Cultural Algorithms 381
15.3 Cultural Evolutionary Programming 384
15.4 The Adaptive Culture Model 387
15.5 Conclusion 393
Problems 395
16 Opposition-Based Learning 397
16.1 Opposition Definitions and Concepts 398
16.1.1 Reflected Opposites and Modulo Opposites 398
16.1.2 Partial Opposites 399
16.1.3 Type 1 Opposites and Type 2 Opposites 401
16.1.4 Quasi Opposites and Super Opposites 402
16.2 Opposition-Based Evolutionary Algorithms 403
16.3 Opposition Probabilities 408
16.4 Jumping Ratio 411
16.5 Oppositional Combinatorial Optimization 413
16.6 Dual Learning 416
16.7 Conclusion 417
Problems 418
17 Other Evolutionary Algorithms 421
17.1 Tabu Search 422
17.2 Artificial Fish Swarm Algorithm 423
17.2.1 Random Behavior 423
17.2.2 Chasing Behavior 424
17.2.3 Swarming Behavior 424
17.2.4 Searching Behavior 425
17.2.5 Leaping Behavior 425
17.2.6 A Summary of the Artificial Fish Swarm Algorithm 426
17.3 Group Search Optimizer 427
17.4 Shuffled Frog Leaping Algorithm 429
17.5 The Firefly Algorithm 431
17.6 Bacterial Foraging Optimization 432
17.7 Artificial Bee Colony Algorithm 435
17.8 Gravitational Search Algorithm 438
17.9 Harmony Search 439
17.10 Teaching-Learning-Based Optimization 441
17.11 Conclusion 444
Problems 446
PART IV SPECIAL TYPES OF OPTIMIZATION PROBLEMS
18 Combinatorial Optimization 449
18.1 The Traveling Salesman Problem 451
18.2 TSP Initialization 452
18.2.1 Nearest-Neighbor Initialization 452
18.2.2 Shortest-Edge Initialization 453
18.2.3 Insertion Initialization 455
18.2.4 Stochastic Initialization 456
18.3 TSP Representations and Crossover 457
18.3.1 Path Representation 457
18.3.2 Adjacency Representation 460
18.3.3 Ordinal Representation 463
18.3.4 Matrix Representation 464
18.4 TSP Mutation 467
18.4.1 Inversion 467
18.4.2 Insertion 467
18.4.3 Displacement 467
18.4.4 Reciprocal Exchange 468
18.5 An Evolutionary Algorithm for the Traveling Salesman Problem 468
18.6 The Graph Coloring Problem 473
18.7 Conclusion 477
Problems 479
19 Constrained Optimization 481
19.1 Penalty Function Approaches 483
19.1.1 Interior Point Methods 483
19.1.2 Exterior Methods 485
19.2 Popular Constraint-Handling Methods 487
19.2.1 Static Penalty Methods 487
19.2.2 Superiority of Feasible Points 487
19.2.3 The Eclectic Evolutionary Algorithm 488
19.2.4 Co-evolutionary Penalties 489
19.2.5 Dynamic Penalty Methods 490
19.2.6 Adaptive Penalty Methods 492
19.2.7 Segregated Genetic Algorithm 492
19.2.8 Self-Adaptive Fitness Formulation 493
19.2.9 Self-Adaptive Penalty Function 494
19.2.10 Adaptive Segregational Constraint Handling 495
19.2.11 Behavioral Memory 496
19.2.12 Stochastic Ranking 497
19.2.13 The Niched-Penalty Approach 498
19.3 Special Representations and Special Operators 499
19.3.1 Special Representations 499
19.3.2 Special Operators 501
19.3.3 Genocop 502
19.3.4 Genocop II 503
19.3.5 Genocop III 503
19.4 Other Approaches to Constrained Optimization 505
19.4.1 Cultural Algorithms 505
19.4.2 Multi-Objective Optimization 506
19.5 Ranking Candidate Solutions 506
19.5.1 Maximum Constraint Violation Ranking 507
19.5.2 Constraint Order Ranking 507
19.5.3 e-Level Comparisons 508
19.6 A Comparison Between Constraint-Handling Methods 508
19.7 Conclusion 511
Problems 515
20 Multi-Objective Optimization 517
20.1 Pareto Optimality 519
20.2 The Goals of Multi-Objective Optimization 523
20.2.1 Hypervolume 525
20.2.2 Relative Coverage 528
20.3 Non-Pareto-Based Evolutionary Algorithms 528
20.3.1 Aggregation Methods 528
20.3.2 The Vector Evaluated Genetic Algorithm (VEGA) 531
20.3.3 Lexicographic Ordering 532
20.3.4 The e-Constraint Method 533
20.3.5 Gender-Based Approaches 534
20.4 Pareto-Based Evolutionary Algorithms 535
20.4.1 Evolutionary Multi-Objective Optimizers 535
20.4.2 The e-Based Multi-Objective Evolutionary Algorithm
(t-MOEA) 537
20.4.3 The Nondominated Sorting Genetic Algorithm (NSGA) 539
20.4.4 The Multi-Objective Genetic Algorithm (MOGA) 542
20.4.5 The Niched Pareto Genetic Algorithm (NPGA) 542
20.4.6 The Strength Pareto Evolutionary Algorithm (SPEA) 544
20.4.7 The Pareto Archived Evolution Strategy (PAES) 551
20.5 Multi-Objective Biogeography-Based Optimization 551
20.5.1 Vector Evaluated BBO 552
20.5.2 Nondominated Sorting BBO 552
20.5.3 Niched Pareto BBO 553
20.5.4 Strength Pareto BBO 554
20.5.5 Multi-Objective BBO Simulations 554
20.6 Conclusion 556
Problems 559
21 Expensive, Noisy, and Dynamic Fitness Functions 563
21.1 Expensive Fitness Functions 564
21.1.1 Fitness Function Approximation 566
21.1.2 Approximating Transformed Functions 576
21.1.3 How to Use Fitness Approximations in Evolutionary
Algorithms 577
21.1.4 Multiple Models 580
21.1.5 Overfitting 582
21.1.6 Evaluating Approximation Methods 583
21.2 Dynamic Fitness Functions 584
21.2.1 The Predictive Evolutionary Algorithm 587
21.2.2 Immigrant Schemes 588
21.2.3 Memory-Based Approaches 593
21.2.4 Evaluating Dynamic Optimization Performance 593
21.3 Noisy Fitness Functions 594
21.3.1 Resampling 596
21.3.2 Fitness Estimation 598
21.3.3 The Kalman Evolutionary Algorithm 598
21.4 Conclusion 600
Problems 603
PART V APPENDICES
Appendix A: Some Practical Advice 607
A.l Check for Bugs 607
A.2 Evolutionary Algorithms are Stochastic 608
A.3 Small Changes can have Big Effects 608
A.4 Big changes can have Small Effects 609
A.5 Populations Have Lots of Information 609
A.6 Encourage Diversity 609
A.7 Use Problem-Specific Information 609
A.8 Save your Results Often 610
A.9 Understand Statistical Significance 610
A. 10 Write Well 610
A. 11 Emphasize Theory 610
A.12 Emphasize Practice 611
Appendix B: The No Free Lunch Theorem and Performance Testing 613
B.l The No Free Lunch Theorem 614
B.2 Performance Testing 621
B.2.1 Overstatements Based on Simulation Results 621
B.2.2 How to Report (and How Not to Report) Simulation Results 623
B.2.3 Random Numbers 628
B.2.4 T-Tests 631
B.2.5 F-Tests 636
B.3 Conclusion 640
Appendix C: Benchmark Optimization Functions 641
C.l Unconstrained Benchmarks 642
C.l.l The Sphere Function 642
C.l.2 The Ackley Function 643
C.l.3 The Ackley Test Function 644
C. 1.4 The Rosenbrock Function 644
C.l.5 The Fletcher Function 645
C.1.6 The Griewank Function 646
C.l.7 The Penalty #1 Function 647
C.1.8 The Penalty #2 Function 647
C.1.9 The Quartic Function 648
C.l. 10 The Tenth Power Function 649
C.l. 11 The Rastrigin Function 650
C.l. 12 The Schwefel Double Sum Function 650
C.l.13 The Schwefel Max Function 651
C.l.14 The Schwefel Absolute Function 652
C.l.15 The Schwefel Sine Function 652
C.l.16 The Step Function 653
C.l.17 The Absolute Function 654
C.l.18 Shekel s Foxhole Function 654
C.l. 19 The Michalewicz Function 655
C.1.20 The Sine Envelope Function 655
C.1.21 The Eggholder Function 656
C.l.22 The Weierstrass Function 657
C.2 Constrained Benchmarks 657
C.2.1 The C01 Function 658
C.2.2 The C02 Function 658
C.2.3 The C03 Function 659
C.2.4 The C04 Function 659
C.2.5 The C05 Function 659
C.2.6 The C06 Function 660
C.2.7 The C07 Function 660
C.2.8 The C08 Function 660
C.2.9 The C09 Function 661
C.2.10 The CIO Function 661
C.2.11 The Cll Function 661
C.2.12 The C12 Function 662
C.2.13 The C13 Function 662
C.2.14 The C14 Function 662
C.2.15 The C15 Function 663
C.2.16 The C16 Function 663
C.2.17 The C17 Function 664
C.2.18 The C18 Function 664
C.2.19 Summary of Constrained Benchmarks 664
C.3 Multi-Objective Benchmarks 665
C.3.1 Unconstrained Multi-Objective Optimization Problem 1 666
C.3.2 Unconstrained Multi-Objective Optimization Problem 2 666
C.3.3 Unconstrained Multi-Objective Optimization Problem 3 667
C.3.4 Unconstrained Multi-Objective Optimization Problem 4 667
C.3.5 Unconstrained Multi-Objective Optimization Problem 5 668
C.3.6 Unconstrained Multi-Objective Optimization Problem 6 668
C.3.7 Unconstrained Multi-Objective Optimization Problem 7 669
C.3.8 Unconstrained Multi-Objective Optimization Problem 8 670
C.3.9 Unconstrained Multi-Objective Optimization Problem 9 670
C.3.10 Unconstrained Multi-Objective Optimization Problem 10 671
C.4 Dynamic Benchmarks 672
C.4.1 The Complete Dynamic Benchmark Description 672
C.4.2 A Simplified Dynamic Benchmark Description 677
C.5 Noisy Benchmarks 677
C.6 Traveling Salesman Problems 678
C.7 Unbiasing the Search Space 680
C.7.1 Offsets 681
C.7.2 Rotation Matrices 682
References 685
Topic Index 727
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| id | DE-604.BV040987364 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:36:52Z |
| institution | BVB |
| isbn | 9780470937419 |
| language | English |
| lccn | 2013000458 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025965222 |
| oclc_num | 846892612 |
| open_access_boolean | |
| owner | DE-384 DE-83 DE-91G DE-BY-TUM DE-29 |
| owner_facet | DE-384 DE-83 DE-91G DE-BY-TUM DE-29 |
| physical | XXX, 742 S. Ill., graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Wiley |
| record_format | marc |
| spelling | Simon, Dan Verfasser aut Evolutionary optimization algorithms biologically-inspired and population-based approaches to computer intelligence Dan Simon Hoboken Wiley 2013 XXX, 742 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Evolutionary computation Computer algorithms Biologically-inspired computing MATHEMATICS / Discrete Mathematics bisacsh Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Softwareentwicklung (DE-588)4116522-6 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 s Softwareentwicklung (DE-588)4116522-6 s DE-604 http://catalogimages.wiley.com/images/db/jimages/9780470937419.jpg Cover image HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025965222&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Simon, Dan Evolutionary optimization algorithms biologically-inspired and population-based approaches to computer intelligence Evolutionary computation Computer algorithms Biologically-inspired computing MATHEMATICS / Discrete Mathematics bisacsh Evolutionärer Algorithmus (DE-588)4366912-8 gnd Softwareentwicklung (DE-588)4116522-6 gnd |
| subject_GND | (DE-588)4366912-8 (DE-588)4116522-6 |
| title | Evolutionary optimization algorithms biologically-inspired and population-based approaches to computer intelligence |
| title_auth | Evolutionary optimization algorithms biologically-inspired and population-based approaches to computer intelligence |
| title_exact_search | Evolutionary optimization algorithms biologically-inspired and population-based approaches to computer intelligence |
| title_full | Evolutionary optimization algorithms biologically-inspired and population-based approaches to computer intelligence Dan Simon |
| title_fullStr | Evolutionary optimization algorithms biologically-inspired and population-based approaches to computer intelligence Dan Simon |
| title_full_unstemmed | Evolutionary optimization algorithms biologically-inspired and population-based approaches to computer intelligence Dan Simon |
| title_short | Evolutionary optimization algorithms |
| title_sort | evolutionary optimization algorithms biologically inspired and population based approaches to computer intelligence |
| title_sub | biologically-inspired and population-based approaches to computer intelligence |
| topic | Evolutionary computation Computer algorithms Biologically-inspired computing MATHEMATICS / Discrete Mathematics bisacsh Evolutionärer Algorithmus (DE-588)4366912-8 gnd Softwareentwicklung (DE-588)4116522-6 gnd |
| topic_facet | Evolutionary computation Computer algorithms Biologically-inspired computing MATHEMATICS / Discrete Mathematics Evolutionärer Algorithmus Softwareentwicklung |
| url | http://catalogimages.wiley.com/images/db/jimages/9780470937419.jpg http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025965222&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT simondan evolutionaryoptimizationalgorithmsbiologicallyinspiredandpopulationbasedapproachestocomputerintelligence |