Multimodal optimization by means of evolutionary algorithms:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer International Publishing
2015
|
| Schriftenreihe: | Natural computing series
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028933286&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028933286&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Umfang: | xx, 189 Seiten Illustrationen (teilweise farbig) |
| ISBN: | 9783319791562 9783319074061 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1819280375491330048 |
|---|---|
| adam_text | Contents
1 Introduction: Towards Multimodal Optimization........................ 1
1.1 Optimization and the Black Box................................... 1
1.1.1 Objective Function and Global Optimum..................... 2
1.1.2 The Locality Principle.................................... 3
1.1.3 Local Optimality........................................ 3
Î. 1.4 Basins of Attraction...................................... 5
1.1.5 Optimization Problem Properties......................... 6
1.1.6 Different Approaches ..................................... 9
1.2 Multimodal Optimization........................................ 11
1.3 Evolutionary Multimodal Optimization............................. 12
1.3.1 Roots.................................................... 13
1.3.2 The Common Framework..................................... 15
1.3.3 Evolution Strategies..................................... 16
1.3.4 EA Techniques few Multimodal Problems.................... 17
1.4 Objectives of This Work........................................ 21
1.5 Book Structure and Usage Guide................................. 22
2 Experimentation in Evolutionary Computation.......................... 27
2.1 Preamble: Justification for a Methodology...................... 27
2.2 The Rise of New Experimentalism in Computer Science......... 29
2.2.1 New Experimentalism and the Top Quark.................... 29
2.2.2 Assessing Algorithms..................................... 30
xiii
XIV
Contents
2.2.3 And What About EC? .................................. 32
2.2.4 Something Different: The Algorithm Engineering Approach? 33
2.3 Deriving an Experimental Methodology.......................... 35
2.3.1 The Basic Methodological Framework..................... 37
2.3.2 Tuning Methods....................................... 43
2.4 Parameters, Adaptability, and Experimental Analysis........... 47
2.4.1 Parameter Tuning or Parameter Control?................. 48
2.4.2 Adaptability .......................................... 49
3 Groundwork for Niching............................................. 55
3.1 Niching and Speciation in Nature............................. 55
3.2 Niching Definitions in Evolutionary Computation............... 56
3.3 Niching Versus Repeated Local Search.......................... 59
3.3.1 A Simple Niching Model............................... 60
3.3.2 Computable Results................................... 63
3.3.3 Simulated Results: Equal Basin Sizes.................. 66
3.3.4 Simulated Results: Unequal Basin Sizes................. 69
3.4 Conclusions................................................ 72
4 Basin Identification by Means of Nearest-Better Clustering......... 75
4.1 Objectives.................................................. 75
4.2 The Basic Nearest-Better Clustering Algorithm................ 77
4.3 Method Choice................................................. 79
4.3.1 Distance Computation............................... 79
4.3.2 Mean Value Detection................................. 81
4.3.3 Connected Components Identification.................. 82
4.4 Correction for Large Sample Sizes and Small Dimensions........ 82
4.4.1 Nearest Neighbor Distances Under Complete Spatial
Randomness............................................ 83
4.4.2 Obtaining an Approximate Nearest Neighbor Distance
Distribution Function................................ 83
4.4.3 When to Apply the Correction....................... 89
4.5 Nearest Better Clustering Extended With a Second Rule......... 89
Contents xv
4.5.1 Deriving a Correction Factor For Rule 2................ 90
4.6 Measuring NBC Performance..................................... 94
4.6.1 Populations, Basins of Attraction, and Clusters........ 94
4.6.2 Test Problems.......................................... 95
4.6.3 Performance Measures................................... 98
4.6.4 Variant Choice and Performance Assessment............ 102
4.7 Conclusions............................................... 113
5 Niching Methods and Multimodal Optimization Performance ...........115
5.1 Use Cases................................................... 116
5.2 Available Performance Measures ............................ 119
5.2.1 Indicators That Require No Problem Knowledge...........120
5.2.2 Indicators That Require Optima Knowledge...............121
5.2.3 Indicators That Require Basin Knowledge................124
5.2.4 A Measure for Real-World Problems: R5S............... 125
5.3 Niching Techniques Overview................................. 127
5.3.1 The Evolutionary Niching Heritage Methods..............129
5.3.2 Cluster Analysis in Global Optimization................130
5.3.3 Explicit Basin Identification in Evolutionary Algorithms ... 132
5.3.4 Comparative Assessment of Niching Method Development . 135
6 Nearest-Better-Based Niching...................................... 139
6.1 Two Niching Methods and Their Parameters.................... 139
6.1.1 Niching Evolutionary Algorithm 1.......................140
6.1.2 Niching Evolutionary Algorithm 2 .................... 142
6.1.3 Parameter Settings and Extensions.................... 142
6.2 Performance Assessment for the One-Global Case ............. 154
6.2.1 Choosing a Set of Multimodal BBOB Test Problems......... 155
6.2.2 Measuring the One-Global Performance...................156
6.3 Performance Assessment for the All-Global Case.............. 161
6.3.1 The CEC 2013 Niching Competition Problems..............161
6.4 Conclusions .................-...............................- 169
XVI
Contents
7 Summary and Final Remarks ........................................171
7.1 Goal 1: Improve the Understanding of Niching in Evolutionary
Algorithms and Evaluate Its Potential Benefits...............171
7.2 Goal 2: Investigate Whether Niching Techniques Are Suitable as
Diagnostic Tools.............................................172
7.3 Goal 3: Compare the Performance of Niching and Canonical
Evolutionary Algorithms......................................173
7.4 Goal 4: Estimate for Which Problem Types Niching EAs Actually
Outperform Canonical EAs .................................. 174
7.5 Conclusions................................................ 174
References........................................................177
Natural Computing Series
Mike Preuss
Multimodal Optimization by Means of Evolutionary Algorithms
This book offers the first comprehensive taxonomy for multimodal optimization
algorithms, work with its root in topics such as niching, parallel evolutionary
algorithms, and global optimization.
The author explains niching in evolutionary algorithms and its benefits; he examines
their suitability for use as diagnostic tools for experimental analysis, especially for
detecting problem (type) properties; and he measures and compares the performances
of niching and canonical EAs using different benchmark test problem sets. His work
consolidates the recent successes in this domain, presenting and explaining use cases,
algorithms, and performance measures, with a focus throughout on the goals of the
optimization processes and a deep understanding of the algorithms used.
The book will be useful for researchers and practitioners in the area of computational
intelligence, particularly those engaged with heuristic search, multimodal optimization,
evolutionary computing, and experimental analysis.
|
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| id | DE-604.BV043517255 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T17:38:26Z |
| institution | BVB |
| isbn | 9783319791562 9783319074061 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028933286 |
| oclc_num | 951534441 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-11 |
| owner_facet | DE-355 DE-BY-UBR DE-11 |
| physical | xx, 189 Seiten Illustrationen (teilweise farbig) |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Springer International Publishing |
| record_format | marc |
| series2 | Natural computing series |
| spellingShingle | Preuß, Mike 1969- Multimodal optimization by means of evolutionary algorithms Evolutionärer Algorithmus (DE-588)4366912-8 gnd Globale Optimierung (DE-588)4140067-7 gnd Multimodalität (DE-588)7859426-1 gnd |
| subject_GND | (DE-588)4366912-8 (DE-588)4140067-7 (DE-588)7859426-1 |
| title | Multimodal optimization by means of evolutionary algorithms |
| title_auth | Multimodal optimization by means of evolutionary algorithms |
| title_exact_search | Multimodal optimization by means of evolutionary algorithms |
| title_full | Multimodal optimization by means of evolutionary algorithms Mike Preuss |
| title_fullStr | Multimodal optimization by means of evolutionary algorithms Mike Preuss |
| title_full_unstemmed | Multimodal optimization by means of evolutionary algorithms Mike Preuss |
| title_short | Multimodal optimization by means of evolutionary algorithms |
| title_sort | multimodal optimization by means of evolutionary algorithms |
| topic | Evolutionärer Algorithmus (DE-588)4366912-8 gnd Globale Optimierung (DE-588)4140067-7 gnd Multimodalität (DE-588)7859426-1 gnd |
| topic_facet | Evolutionärer Algorithmus Globale Optimierung Multimodalität |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028933286&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028933286&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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