Theory of evolutionary computation: recent developments in discrete optimization
Gespeichert in:
| Weitere Verfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2020]
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| Schriftenreihe: | Natural computing series
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xii, 518 Seiten Illustrationen |
| ISBN: | 9783030294168 |
Internformat
MARC
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| 653 | |a MÉTHODES DE PROGRAMMATION SPÉCIFIQUES | ||
| 653 | |a SPECIAL PROGRAMMING METHODS | ||
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Datensatz im Suchindex
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| adam_text | Contents 1 Probabilistic Tools for the Analysis ofRandomized Optimization Heuristics ............................................................... Benjamin Doerr 1.1 Introduction.............................................................................. 1.2 Notation.................................................................................... 1.3 Elementary Probability Theory.............................................. 1.4 Useful Inequalities..................................................................... 1.5 Union Bound............................................................................ 1.6 Expectation and Variance........................................................ 1.7 Conditioning ............................................................................ 1.8 Stochastic Domination and Coupling .................................... 1.9 The Coupon Collector Process................................................ 1.10 Large-Deviation Bounds........................................................... References............................................................................................ 1 1 3 3 5 11 15 22 25 35 38 80 2 Drift Analysis .................................................................................. 89 Johannes Lengler 2.1 Introduction.............................................................................. 89 2.2 Basics of Drift Analysis........................................................... 90 2.3 Elementary Introduction to Drift Analysis............................. 94 2.4 Advanced Drift
Theorems......................................................... 112 2.5 Finding the Potential Function..................................................123 2.6 Conclusion.................................................................................. 125 References ..............................................................................................126 3 Complexity Theory for Discrete Black-BoxOptimization Heuristics ............................................................................................133 Carola Doerr 3.1 Introduction and Historical Remarks........................................ 134 3.2 The Unrestricted Black-Box Model.......................................... 138 3.3 Known Black-Box Complexities in the Unrestricted Model.. 148 vii
Contents viii 3.4 3.5 3.6 3.7 3.8 Memory-Restricted Black-Box Complexity................................162 Comparison- and Ranking-Based Black-Box Complexity.... 166 Unbiased Black-Box Complexity ................................................170 Combined Black-Box Complexity Models..................................188 Summary of Known Black-Box Complexities for OneMax and LeadingOnes........................................................................196 3.9 From Black-Box Complexity to Algorithm Design..................197 3.10 From Black-Box Complexity to Mastermind............................201 3.11 Conclusion and Selected Open Problems..................................203 References................................................................................................. 206 4 Parameterized Complexity Analysis of Randomized Search Heuristics................................................................................. 213 Frank Neumann and Andrew M. Sutton 4.1 Introduction................................................................................... 213 4.2 Parameterized Complexity Analysis............................................216 4.3 Maximum-Leaf Spanning Trees ..................................................217 4.4 Minimum Vertex Cover............................................................... 221 4.5 Submodular Functions with Constraints....................................226 4.6 Euclidean TSP............................................................................... 230 4.7
Conclusion..................................................................................... 243 References................................................................................................. 244 5 Analysing Stochastic Search Heuristics Operating on a Fixed Budget......................................................................................... 249 Thomas Jansen 5.1 Introduction................................................................................... 249 5.2 Analytical Perspective and Basic Results..................................251 5.3 Reusing Known Runtime Results................................................255 5.4 Advanced Methods....................................................................... 257 5.5 Results Obtained by Using the Fixed-Budget Perspective... 261 5.6 Summary ....................................................................................... 266 References................................................................................................. 268 6 Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices............................................................271 Benjamin Doerr and Carola Doerr 6.1 Introduction................................................................................... 271 6.2 A Motivating Example: (1+1) EA and RLS on LeadingOnes273 6.3 Classification of Parameter Control Mechanisms......................275 6.4 State-Dependent Parameter Control..........................................278 6.5 Success-
Based Parameter Control ..............................................287 6.6 Learning-Inspired Parameter Control ........................................295 6.7 Self-Adaptation: Endogenous Parameter Control....................299 6.8 Hyper-Heuristics........................................................................... 303
Contents ix 6.9 Conclusion and Outlook.............................................................312 References..............................................................................................315 7 Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments.................................................................323 Prank Neumann, Mojgan Pourhassan and Vahid Roostapour 7.1 Introduction................................................................................ 323 7.2 Preliminaries .............................................................................. 325 7.3 Analysis of Evolutionary Algorithms on Dynamic Problems . 330 7.4 Analysis of Evolutionary Algorithms on Stochastic Problems 342 7.5 Ant Colony Optimization ......................................................... 351 7.6 Conclusions ................................................................................ 353 References ............................................................................................. 354 8 The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses........359 Dirk Sudholt 8.1 Introduction................................................................................ 359 8.2 Preliminaries.............................................................................. 362 8.3 How Diversity Benefits Global Exploration.............................363 8.4 How Diversity Benefits Crossover..............................................381 8.5 How Diversity Benefits Dynamic
Optimization.......................392 8.6 Diversity-Based Parent Selection..............................................396 8.7 Conclusions ................................................................................ 399 References ............................................................................................. 400 9 Theory of Estimation-of-Distribution Algorithms................. 405 Martin S. Krejca and Carsten Witt 9.1 Introduction................................................................................ 405 9.2 Estimation-of-Distribution Algorithms.................................... 407 9.3 Common Fitness Functions....................................................... 418 9.4 Convergence Analyses.................................................................419 9.5 Runtime Analyses.......................................................................422 9.6 Conclusions and Open Problems ..............................................437 References ............................................................................................. 438 10 Theoretical Foundations of Immune-Inspired Randomized Search Heuristics for Optimization ............................................443 Christine Zarges 10.1 Introduction................................................................................ 443 10.2 Theoretical Analyses of Hypermutations ................................ 445 10.3 Theoretical Analyses of Aging Operators................................ 457 10.4 Theoretical Analyses of Complete AIS .................................... 462 10.5
Summary ....................................................................................469 References ............................................................................................. 470
x Contents 11 Computational Complexity Analysis of Genetic Programming................................................................................. 475 Andrei Lissovoi and Pietro S. Oliveto 11.1 Introduction................................................................................... 476 11.2 Preliminaries ................................................................................. 478 11.3 Evolving Tree Structures..............................................................482 11.4 Evolving Programs of Fixed Size.................................................494 11.5 Evolving Proper Programs:Boolean Functions......................... 498 11.6 Other GP Algorithms....................................................................509 11.7 Conclusion..................................................................................... 513 References ................................................................................................. 514
|
| adam_txt |
Contents 1 Probabilistic Tools for the Analysis ofRandomized Optimization Heuristics . Benjamin Doerr 1.1 Introduction. 1.2 Notation. 1.3 Elementary Probability Theory. 1.4 Useful Inequalities. 1.5 Union Bound. 1.6 Expectation and Variance. 1.7 Conditioning . 1.8 Stochastic Domination and Coupling . 1.9 The Coupon Collector Process. 1.10 Large-Deviation Bounds. References. 1 1 3 3 5 11 15 22 25 35 38 80 2 Drift Analysis . 89 Johannes Lengler 2.1 Introduction. 89 2.2 Basics of Drift Analysis. 90 2.3 Elementary Introduction to Drift Analysis. 94 2.4 Advanced Drift
Theorems. 112 2.5 Finding the Potential Function.123 2.6 Conclusion. 125 References .126 3 Complexity Theory for Discrete Black-BoxOptimization Heuristics .133 Carola Doerr 3.1 Introduction and Historical Remarks. 134 3.2 The Unrestricted Black-Box Model. 138 3.3 Known Black-Box Complexities in the Unrestricted Model. 148 vii
Contents viii 3.4 3.5 3.6 3.7 3.8 Memory-Restricted Black-Box Complexity.162 Comparison- and Ranking-Based Black-Box Complexity. 166 Unbiased Black-Box Complexity .170 Combined Black-Box Complexity Models.188 Summary of Known Black-Box Complexities for OneMax and LeadingOnes.196 3.9 From Black-Box Complexity to Algorithm Design.197 3.10 From Black-Box Complexity to Mastermind.201 3.11 Conclusion and Selected Open Problems.203 References. 206 4 Parameterized Complexity Analysis of Randomized Search Heuristics. 213 Frank Neumann and Andrew M. Sutton 4.1 Introduction. 213 4.2 Parameterized Complexity Analysis.216 4.3 Maximum-Leaf Spanning Trees .217 4.4 Minimum Vertex Cover. 221 4.5 Submodular Functions with Constraints.226 4.6 Euclidean TSP. 230 4.7
Conclusion. 243 References. 244 5 Analysing Stochastic Search Heuristics Operating on a Fixed Budget. 249 Thomas Jansen 5.1 Introduction. 249 5.2 Analytical Perspective and Basic Results.251 5.3 Reusing Known Runtime Results.255 5.4 Advanced Methods. 257 5.5 Results Obtained by Using the Fixed-Budget Perspective. 261 5.6 Summary . 266 References. 268 6 Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices.271 Benjamin Doerr and Carola Doerr 6.1 Introduction. 271 6.2 A Motivating Example: (1+1) EA and RLS on LeadingOnes273 6.3 Classification of Parameter Control Mechanisms.275 6.4 State-Dependent Parameter Control.278 6.5 Success-
Based Parameter Control .287 6.6 Learning-Inspired Parameter Control .295 6.7 Self-Adaptation: Endogenous Parameter Control.299 6.8 Hyper-Heuristics. 303
Contents ix 6.9 Conclusion and Outlook.312 References.315 7 Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments.323 Prank Neumann, Mojgan Pourhassan and Vahid Roostapour 7.1 Introduction. 323 7.2 Preliminaries . 325 7.3 Analysis of Evolutionary Algorithms on Dynamic Problems . 330 7.4 Analysis of Evolutionary Algorithms on Stochastic Problems 342 7.5 Ant Colony Optimization . 351 7.6 Conclusions . 353 References . 354 8 The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses.359 Dirk Sudholt 8.1 Introduction. 359 8.2 Preliminaries. 362 8.3 How Diversity Benefits Global Exploration.363 8.4 How Diversity Benefits Crossover.381 8.5 How Diversity Benefits Dynamic
Optimization.392 8.6 Diversity-Based Parent Selection.396 8.7 Conclusions . 399 References . 400 9 Theory of Estimation-of-Distribution Algorithms. 405 Martin S. Krejca and Carsten Witt 9.1 Introduction. 405 9.2 Estimation-of-Distribution Algorithms. 407 9.3 Common Fitness Functions. 418 9.4 Convergence Analyses.419 9.5 Runtime Analyses.422 9.6 Conclusions and Open Problems .437 References . 438 10 Theoretical Foundations of Immune-Inspired Randomized Search Heuristics for Optimization .443 Christine Zarges 10.1 Introduction. 443 10.2 Theoretical Analyses of Hypermutations . 445 10.3 Theoretical Analyses of Aging Operators. 457 10.4 Theoretical Analyses of Complete AIS . 462 10.5
Summary .469 References . 470
x Contents 11 Computational Complexity Analysis of Genetic Programming. 475 Andrei Lissovoi and Pietro S. Oliveto 11.1 Introduction. 476 11.2 Preliminaries . 478 11.3 Evolving Tree Structures.482 11.4 Evolving Programs of Fixed Size.494 11.5 Evolving Proper Programs:Boolean Functions. 498 11.6 Other GP Algorithms.509 11.7 Conclusion. 513 References . 514 |
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| spelling | Theory of evolutionary computation recent developments in discrete optimization Benjamin Doerr, Frank Neumann, editors Cham, Switzerland Springer [2020] xii, 518 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Natural computing series Komplexitätstheorie (DE-588)4120591-1 gnd rswk-swf Diskrete Optimierung (DE-588)4150179-2 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf SPEZIELLE PROGRAMMIERMETHODEN MÉTHODES DE PROGRAMMATION SPÉCIFIQUES SPECIAL PROGRAMMING METHODS Evolutionary computation (DE-588)4143413-4 Aufsatzsammlung gnd-content Komplexitätstheorie (DE-588)4120591-1 s Evolutionärer Algorithmus (DE-588)4366912-8 s Diskrete Optimierung (DE-588)4150179-2 s DE-604 Doerr, Benjamin ca. 20./21. Jahrhundert (DE-588)1204931011 edt Neumannm, Frank (DE-588)1232222895 edt Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032614915&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Theory of evolutionary computation recent developments in discrete optimization Komplexitätstheorie (DE-588)4120591-1 gnd Diskrete Optimierung (DE-588)4150179-2 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| subject_GND | (DE-588)4120591-1 (DE-588)4150179-2 (DE-588)4366912-8 (DE-588)4143413-4 |
| title | Theory of evolutionary computation recent developments in discrete optimization |
| title_auth | Theory of evolutionary computation recent developments in discrete optimization |
| title_exact_search | Theory of evolutionary computation recent developments in discrete optimization |
| title_exact_search_txtP | Theory of evolutionary computation recent developments in discrete optimization |
| title_full | Theory of evolutionary computation recent developments in discrete optimization Benjamin Doerr, Frank Neumann, editors |
| title_fullStr | Theory of evolutionary computation recent developments in discrete optimization Benjamin Doerr, Frank Neumann, editors |
| title_full_unstemmed | Theory of evolutionary computation recent developments in discrete optimization Benjamin Doerr, Frank Neumann, editors |
| title_short | Theory of evolutionary computation |
| title_sort | theory of evolutionary computation recent developments in discrete optimization |
| title_sub | recent developments in discrete optimization |
| topic | Komplexitätstheorie (DE-588)4120591-1 gnd Diskrete Optimierung (DE-588)4150179-2 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| topic_facet | Komplexitätstheorie Diskrete Optimierung Evolutionärer Algorithmus Aufsatzsammlung |
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