Handbook of memetic algorithms:
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| Other Authors: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg
Springer
2012
|
| Series: | Studies in computational intelligence
379 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XXVI, 368 S. Ill., graph. Darst. |
| ISBN: | 9783642232466 3642232469 |
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IMAGE 1
CONTENTS
PART I: FOUNDATIONS 1 BASIC CONCEPTS 3
FERRANTE NERI, CARLOS COTTA 1.1 WHAT IS OPTIMIZATION? 3
1.2 OPTIMIZATION CAN BE HARD 5
1.3 USING METAHEURISTICS 6
2 EVOLUTIONARY ALGORITHMS 9
AGOSTON E. EIBEN, JAMES E. SMITH 2.1 MOTIVATION AND BRIEF HISTORY 9
2.2 WHAT IS AN EVOLUTIONARY ALGORITHM? 10
2.3 COMPONENTS OF EVOLUTIONARY ALGORITHMS 12
2.3.1 REPRESENTATION (DEFINITION OF INDIVIDUALS) 13
2.3.2 EVALUATION FUNCTION (FITNESS FUNCTION) 14
2.3.3 POPULATION 14
2.3.4 PARENT SELECTION MECHANISM 15
2.3.5 VARIATION OPERATORS 15
2.3.6 SURVIVOR SELECTION MECHANISM (REPLACEMENT) 17 2.3.7 INITIALISATION
17
2.3.8 TERMINATION CONDITION 18
2.4 THE OPERATION OF AN EVOLUTIONARY ALGORITHM 18
2.5 EVOLUTIONARY ALGORITHM VARIANTS 21
2.6 DESIGNING AND TUNING EVOLUTIONARY ALGORITHMS 25
2.7 CONCLUDING REMARKS 27
3 LOCAL SEARCH 29
MARCO A. MONTES DE OCA, CARLOS COTTA, FERRANTE NERI 3.1 BASIC CONCEPTS
29
3.2 NEIGHBORHOODS AND LOCAL OPTIMA 30
3.3 CLASSIFICATIONS OF LOCAL SEARCH 31
3.4 LOCAL SEARCH IN COMBINATORIAL DOMAINS 33
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/101351081X
DIGITALISIERT DURCH
IMAGE 2
CONTENTS
3.4.1 HILL CLIMBING 34
3.4.2 SIMULATED ANNEALING 35
3.4.3 TABU SEARCH 36
3.5 LOCAL SEARCH IN CONTINUOUS DOMAINS 36
3.5.1 CLASSIFICATION OF LOCAL SEARCH TECHNIQUES FOR CONTINUOUS DOMAINS
37
3.5.2 COMMONLY USED LOCAL SEARCH TECHNIQUES IN MEMETIC ALGORITHMS FOR
CONTINUOUS DOMAINS 38
4 A PRIMER ON MEMETIC ALGORITHMS 43
FERRANTE NERI, CARLOS COTTA 4.1 INTRODUCTION 43
4.2 THE NEED FOR MEMETIC ALGORITHMS 44
4.3 A BASIC MEMETIC ALGORITHM TEMPLATE 46
4.4 DESIGN ISSUES 49
4.5 CONCLUSIONS AND OUTLOOK 50
4.6 MEMETIC ALGORITHMS AND MEMETIC COMPUTING 51
PART II: METHODOLOGY
5 PARAMETRIZATION AND BALANCING LOCAL AND GLOBAL SEARCH 55 DIRK SUDHOLT
5.1 INTRODUCTION 55
5.2 BALANCING GLOBAL AND LOCAL SEARCH 56
5.2.1 EARLY WORKS AND THE EFFECT OF LOCAL SEARCH 56
5.2.2 ASPECTS THAT DETERMINE THE OPTIMAL BALANCE 58 5.2.3 HOW TO FIND AN
OPTIMAL BALANCE 60
5.3 TIME COMPLEXITY OF LOCAL SEARCH 61
5.3.1 POLYNOMIAL AND EXPONENTIAL TIMES TO LOCAL OPTIMALITY 62
5.3.2 INTRACTABILITY OF LOCAL SEARCH PROBLEMS 63
5.4 FUNCTIONS WITH SUPERPOLYNOMIAL PERFORMANCE GAPS 66 5.4.1 FUNCTIONS
WHERE THE LOCAL SEARCH DEPTH IS ESSENTIAL 67
5.4.2 FUNCTIONS WHERE THE LOCAL SEARCH FREQUENCY IS ESSENTIAL 69
5.5 CONCLUSIONS 71
6 MEMETIC ALGORITHMS IN DISCRETE OPTIMIZATION 73
JIN-KAO HAO 6.1 INTRODUCTION 73
6.2 SURVEY OF MEMETIC ALGORITHMS FOR DISCRETE OPTIMIZATION 74 6.2.1
RATIONALE 74
6.2.2 MEMETIC ALGORITHMS IN OVERVIEW 75
6.2.3 PERFORMANCE OF MEMETIC ALGORITHMS FOR DISCRETE OPTIMIZATION 77
IMAGE 3
CONTENTS XV
6.3 SPECIAL DESIGN CONSIDERATIONS 77
6.3.1 DESIGN OF DEDICATED LOCAL SEARCH 77
6.3.2 DESIGN OF SEMANTIC COMBINATION OPERATOR 81
6.3.3 POPULATION DIVERSITY MANAGEMENT 83
6.3.4 OTHER ISSUES 85
6.4 CASE STUDIES 86
6.4.1 GRAPH COLORING PROBLEMS 87
6.4.2 MAXIMUM PARSIMONY PHYLOGENY 89
6.5 CONCLUSIONS 93
7 MEMETIC ALGORITHMS AND FITNESS LANDSCAPES IN COMBINATORIAL
OPTIMIZATION 95
PETER MERZ 7.1 INTRODUCTION 95
7.2 MAS IN COMBINATORIAL OPTIMIZATION 95
7.2.1 COMBINATORIAL OPTIMIZATION 96
7.2.2 MA OUTLINE 96
7.2.3 RELATED META-HEURISTICS 98
7.3 WHY AND WHEN MAS WORK 98
7.3.1 THE CONCEPT OF FITNESS LANDSCAPES 99
7.3.2 IV/ST-LANDSCAPES 99
7.3.3 ANALYSIS OF FITNESS LANDSCAPES 100
7.4 CASE STUDY I: THE TSP 105
7.4.1 FITNESS LANDSCAPE 106
7.4.2 STATE-OF-THE-ART META-HEURISTICS FOR THE TSP 108 7.5 CASE STUDY
II: THE BQP I LL
7.5.1 FITNESS LANDSCAPE 112
7.5.2 STATE-OF-THE-ART META-HEURISTICS FOR THE BQP 115 7.5.3 A MEMETIC
ALGORITHM USING INNOVATIVE RECOMBINATION 116
7.6 CONCLUSION 119
8 MEMETIC ALGORITHMS IN CONTINUOUS OPTIMIZATION 121
CARLOS COTTA, FERRANTE NERI 8.1 INTRODUCTION AND BASIC CONCEPTS 121
8.2 GLOBAL AND LOCAL CONTINUOUS OPTIMIZATION 122
8.3 GLOBAL OPTIMIZATION ALGORITHMS 123
8.3.1 STOCHASTIC GLOBAL SEARCH, BRUTE FORCE AND RANDOM WALK 124
8.3.2 EVOLUTION STRATEGY AND REAL CODED EVOLUTIONARY ALGORITHMS 124
8.3.3 PARTICLE SWARM OPTIMIZATION 127
8.3.4 DIFFERENTIAL EVOLUTION 129
8.4 PARTICULARITIES OF MEMETIC APPROACHES FOR CONTINUOUS OPTIMIZATION
131
IMAGE 4
CONTENTS
9 MEMETIC ALGORITHMS IN CONSTRAINED OPTIMIZATION 135
TAPABRATA RAY, RUHUL SARKER 9.1 INTRODUCTION 135
9.2 CONSTRAINED OPTIMIZATION 136
9.3 CLASSIFICATION OF MAS 138
9.4 MAS WITH CONVENTIONAL REPRESENTATION 138
9.5 MAS WITH ALTERNATIVE REPRESENTATIONS 140
9.6 NUMERICAL CASE STUDIES 142
9.6.1 CASE STUDY 1: INFEASIBILITY EMPOWERED MEMETIC ALGORITHM FOR
CONSTRAINED OPTIMIZATION PROBLEMS: MA WITH CONVENTIONAL REPRESENTATION
142
9.6.2 CASE STUDY 2: MA WITH ALTERNATIVE REPRESENTATION 147 9.7 SUMMARY
AND CONCLUSIONS 151
10 DIVERSITY MANAGEMENT IN MEMETIC ALGORITHMS 153
FERRANTE NERI 10.1 INTRODUCTION 153
10.2 HANDLING THE DIVERSITY OF MEMETIC ALGORITHMS: A SHORT SURVEY 154
10.3 FITNESS DIVERSITY ADAPTATION 157
10.3.1 FITNESS DIVERSITY METRICS 158
10.3.2 COORDINATION OF THE SEARCH: THE "NATURA NON FACIT SALTUS"
PRINCIPLE 162
10.4 CONCLUSION 164
11 SELF-ADAPTATIVE AND COEVOLVING MEMETIC ALGORITHMS 167 JAMES E. SMITH
11.1 INTRODUCTION 167
11.2 BACKGROUND 168
11.2.1 MAS WITH MULTIPLE LS OPERATORS 168
11.2.2 SELF-ADAPTATION IN EAS 169
11.2.3 CO-EVOLUTIONARY SYSTEMS 169
11.3 A FRAMEWORK FOR SELF-ADAPTION AND CO-EVOLUTION OF MEMES AND GENES
170
11.3.1 SPECIFYING LOCAL SEARCH 171
11.3.2 ADAPTING THE SPECIFICATION OF LOCAL SEARCH 171 11.4 TEST SUIT AND
METHODOLOGY 173
11.4.1 THE TEST SUITE 174
11.4.2 EXPERIMENTAL SET-UP AND TERMINOLOGY 174
11.5 SELF-ADAPTATION OF FIXED AND VARYING SIZED RULES 175 11.5.1
SELF-ADAPTING THE CHOICE FROM A FIXED SET OF MEMES. 175 11.5.2
SELF-ADAPTATION OF MEME DEFINITIONS 176
11.5.3 RESULTS ON TRAP FUNCTIONS 176
11.5.4 ANALYSIS OF RESULTS AND EVOLUTION OF RULE BASE 177 11.5.5
BENCHMARKING THE SELF-ADAPTIVE SYSTEMS 178 11.5.6 SUMMARY OF
SELF-ADAPTIVE RESULTS 181
IMAGE 5
CONTENTS XVII
11.6 EXTENSION TO TRUE CO-EVOLUTION: THE CREDIT ASSIGNMENT PROBLEM 181
11.6.1 RESULTS: RELIABILITY 182
11.6.2 RESULTS: EFFICIENCY 184
11.7 VARYING THE POPULATION SIZES 185
11.8 CONCLUSIONS 188
12 MEMETIC ALGORITHMS AND COMPLETE TECHNIQUES 189
CARLOS COTTA, ANTONIO J. FERNANDEZ LEIVA, JOSE E. GALLARDO 12.1
INTRODUCTION 189
12.2 BACKGROUND 190
12.3 CLASSIFICATION OF HYBRIDIZATION APPROACHES 191
12.4 INTEGRATIVE COMBINATIONS 192
12.5 COLLABORATIVE COMBINATIONS 195
12.6 CONCLUSIONS 199
13 MULTIOBJECTIVE MEMETIC ALGORITHMS 201
ANDRZEJ JASZKIEWICZ, HISAO ISHIBUCHI, QINGFU ZHANG 13.1 INTRODUCTION 201
13.2 BASIC DEFINITION AND CONCEPTS 202
13.2.1 BASIC CONCEPTS 202
13.2.2 AGGREGATION FUNCTIONS 204
13.2.3 WEIGHTED SUM APPROACH 204
13.2.4 TCHEBYCHEFF APPROACH 205
13.3 ADAPTATION OF MEMETIC ALGORITHMS FOR MULTIOBJECTIVE OPTIMIZATION -
BASIC CONCEPTS 205
13.3.1 DOMINANCE-BASED EVALUATION MECHANISMS 206 13.3.2 AGGREGATION
FUNCTION-BASED EVALUATION MECHANISMS 207
13.3.3 PROBLEM LANDSCAPES IN MULTIOBJECTIVE OPTIMIZATION . . 208 13.3.4
ARCHIVE OF POTENTIALLY PARETO-OPTIMAL SOLUTIONS 209 13.3.5 EVALUATION OF
MULTIOBJECTIVE MEMETIC ALGORITHMS 209 13.4 EXAMPLES OF MULTIOBJECTIVE
MEMETIC ALGORITHMS 210
13.4.1 MOGLS OF ISHIBUCHI AND MURATA 210
13.4.2 M-PAES 210
13.4.3 NSGA-II WITH LS 211
13.4.4 MOGLS OF JASZKIEWICZ 211
13.4.5 RM-MEDA 212
13.4.6 MOEA/D 213
13.4.7 MGK POPULATION HEURISTIC 213
13.4.8 MEMETIC APPROACH BY CHEN AND CHEN 214
13.4.9 SPEA2WITHLS 214
13.4.10 INTERACTIVE MEMETIC ALGORITHM BY DIAS ET AL 214 13.4.11 SMS-EMOA
WITH LOCAL SEARCH 214
13.5 IMPLEMENTATION OF MULTIOBJECTIVE MEMETIC ALGORITHMS 214 13.6
CONCLUSIONS 217
IMAGE 6
XVIII CONTENTS
14 MEMETIC ALGORITHMS IN THE PRESENCE OF UNCERTAINTIES 219 YOEL TENNE
14.1 MOTIVATION 219
14.2 UNCERTAINTY DUE TO APPROXIMATION 220
14.3 UNCERTAINTY DUE TO ROBUSTNESS 224
14.4 UNCERTAINTY DUE TO NOISE 229
14.5 UNCERTAINTY DUE TO TIME-DEPENDENCY 232
14.6 CONCLUSION 237
PART III: APPLICATIONS
15 MEMETIC ALGORITHMS IN ENGINEERING AND DESIGN 241
ANDREA CAPONIO, FERRANTE NERI 15.1 INTRODUCTION 241
15.2 APPLICATIONS OF MAS IN ENGINEERING PROBLEMS 242
15.2.1 ENGINEERING APPLICATIONS IN SINGLE-OBJECTIVE OPTIMIZATION 242
15.2.2 ENGINEERING APPLICATIONS IN MULTI-OBJECTIVE OPTIMIZATION 252
15.3 A STUDY CASE: THE FAST ADAPTIVE MEMETIC ALGORITHM 255 15.3.1 AN
INSIGHT INTO THE PROBLEM 255
15.3.2 FAST ADAPTIVE MEMETIC ALGORITHM 257
15.4 CONCLUSIONS 259
16 MEMETIC ALGORITHMS IN BIOINFORMATICS 261
REGINA BERRETTA, CARLOS COTTA, PABLO MOSCATO 16.1 INTRODUCTION 261
16.2 MICROARRAY DATA ANALYSIS 262
16.2.1 CLUSTERING 264
16.2.2 FEATURE SELECTION 265
16.3 PHYLOGENETICS 266
16.4 PROTEIN STRUCTURE ANALYSIS AND MOLECULAR DESIGN 267 16.5 SEQUENCE
ANALYSIS 269
16.6 SYSTEMS BIOLOGY 270
PART IV: EPILOGUE
17 MEMETIC ALGORITHMS: THE UNTOLD STORY 275
PABLO MOSCATO 17.1 MOTIVATION, OR SOMETHING LIKE THAT 275
17.2 IN THE BEGINNING, THERE WAS NO EVOLUTIONARY COMPUTATION 276 17.3
CALTECH AND THE RED DOOR CAFE 281
17.4 LANDSCAPES AND THE CORRELATION OF LOCAL OPTIMA 285 17.5
HIERARCHICAL OBJECTIVE FUNCTIONS AND MEMETIC ALGORITHMS THAT RUN ON A
"SEGMENT" 289
17.6 A ROYAL VISIT TO ARGENTINA 292
IMAGE 7
CONTENTS XIX
17.7 TO BRAZIL, WITHOUT THE BEACHES 296
17.8 FIXED-PARAMETER TRACTABILITY, AND THE COMPLEXITY OF RECOMBINATION
299
17.9 NEWCASTLE, AUSTRALIA, AND BIOMEDICAL RESEARCH CLOSER TO THE BEACH
303
17.10 FUTURE OPPORTUNITIES (IF WE CONSTRAIN THE BEAST) 305
REFERENCES 311
AUTHOR INDEX 361
SUBJECT INDEX 363 |
| any_adam_object | 1 |
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| dewey-hundreds | 000 - Computer science, information, general works 500 - Natural sciences and mathematics |
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| dewey-raw | 006.3 519.7 |
| dewey-search | 006.3 519.7 |
| dewey-sort | 16.3 |
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| discipline | Informatik Mathematik |
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| spelling | Handbook of memetic algorithms Ferrante Neri ... (ed.) Berlin ; Heidelberg Springer 2012 XXVI, 368 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 379 Literaturangaben Optimierungsproblem (DE-588)4390818-4 gnd rswk-swf Memetischer Algorithmus (DE-588)4625421-3 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Memetischer Algorithmus (DE-588)4625421-3 s Optimierungsproblem (DE-588)4390818-4 s DE-604 Neri, Ferrante edt Erscheint auch als Online-Ausgabe Handbook of Memetic Algorithms Studies in computational intelligence 379 (DE-604)BV020822171 379 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3853494&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=024622224&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Handbook of memetic algorithms Studies in computational intelligence Optimierungsproblem (DE-588)4390818-4 gnd Memetischer Algorithmus (DE-588)4625421-3 gnd |
| subject_GND | (DE-588)4390818-4 (DE-588)4625421-3 (DE-588)4143413-4 |
| title | Handbook of memetic algorithms |
| title_auth | Handbook of memetic algorithms |
| title_exact_search | Handbook of memetic algorithms |
| title_full | Handbook of memetic algorithms Ferrante Neri ... (ed.) |
| title_fullStr | Handbook of memetic algorithms Ferrante Neri ... (ed.) |
| title_full_unstemmed | Handbook of memetic algorithms Ferrante Neri ... (ed.) |
| title_short | Handbook of memetic algorithms |
| title_sort | handbook of memetic algorithms |
| topic | Optimierungsproblem (DE-588)4390818-4 gnd Memetischer Algorithmus (DE-588)4625421-3 gnd |
| topic_facet | Optimierungsproblem Memetischer Algorithmus Aufsatzsammlung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3853494&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=024622224&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT neriferrante handbookofmemeticalgorithms |