Availability: Evolutionary algorithms for solving multi-objective problems

Evolutionary algorithms for solving multi-objective problems:

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Bibliographic Details
Main Authors:	Coello Coello, Carlos A. 1967- (Author), Lamont, Gary B. (Author), Van Veldhuizen, David A. (Author)
Format:	Kit Book
Language:	English
Published:	New York, NY Springer 2007
Edition:	Second edition
Series:	Genetic and evolutionary computation series
Subjects:	Evolutionärer Algorithmus Mehrkriterielle Optimierung Evolutionäre Systementwicklung
Links:	http://deposit.dnb.de/cgi-bin/dokserv?id=2813140&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=015862047&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Physical Description:	XXI, 800 S. graph. Darst.
ISBN:	0387332545 9780387332543 9780387367972

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Record in the Search Index

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adam_text	CARLOS A. COELLO COELLO GARY B. LAMONT DAVID A. VAN VELDHUIZEN EVOLUTIONARY ALGORITHMS FOR SOLVING MULTI-OBJECTIVE PROBLEMS SECOND EDITION 5)SPRI RINGER CONTENTS 1 BASIC CONCEPTS 1 1.1 INTRODUCTION 1 1.2 DEFINITIONS 3 1.2.1 SINGLE-OBJECTIVE OPTIMIZATION 4 1.2.2 THE MULTIOBJECTIVE OPTIMIZATION PROBLEM 5 1.2.3 MULTIOBJECTIVE OPTIMIZATION PROBLEM 7 1.2.4 DEFINITION OF MOEA PROGRESS 14 1.2.5 COMPUTATIONAL DOMAIN IMPACT 14 1.2.6 PARETO EPSILON MODEL 17 1.2.7 DECISION MAKER IMPACT 18 1.3 AN EXAMPLE 19 1.4 GENERAL OPTIMIZATION ALGORITHM OVERVIEW 21 1.5 EA BASICS 24 1.6 ORIGINS OF MULTIOBJECTIVE OPTIMIZATION 29 1.6.1 MATHEMATICAL FOUNDATIONS 30 1.6.2 EARLY APPLICATIONS 30 1.7 CLASSIFYING TECHNIQUES 31 1.7.1 A PRIORI PREFERENCE ARTICULATION 32 1.7.2 A POSTERIORI PREFERENCE ARTICULATION 46 1.7.3 PROGRESSIVE PREFERENCE ARTICULATION 47 1.8 USING EVOLUTIONARY ALGORITHMS 51 1.8.1 PARETO NOTATION 53 1.8.2 MOEA CLASSIFICATION 54 1.9 SUMMARY 55 FURTHER EXPLORATIONS 57 2 MOP EVOLUTIONARY ALGORITHM APPROACHES 6 1 2.1 INTRODUCTION 61 2.2 MOEA TECHNIQUES 63 2.2.1 A PRIORI TECHNIQUES 65 CONTENTS 2.2.2 PROGRESSIVE TECHNIQUES 70 2.2.3 A POSTERIORI TECHNIQUES 71 2.2.4 GENERIC MOEA GOALS AND OPERATOR DESIGN 77 2.3 STRUCTURES OF VARIOUS MOEAS 88 2.3.1 MULTI-OBJECTIVE GENETIC ALGORITHM (MOGA) 88 2.3.2 NONDOMINATED SORTING GENETIC ALGORITHM (NSGA) 91 2.3.3 NICHED-PARETO GENETIC ALGORITHM (NPGA) 94 2.3.4 PARETO ARCHIVED EVOLUTION STRATEGY (PAES) 95 2.3.5 STRENGTH PARETO EVOLUTIONARY ALGORITHM (SPEA) 97 2.3.6 MULTIOBJECTIVE MESSY GENETIC ALGORITHM (MOMGA) ... 99 2.3.7 PARETO ENVELOPE-BASED SELECTION ALGORITHM (PESA) ... 101 2.3.8 THE MICRO-GENETIC ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION 102 2.3.9 MULTIOBJECTIVE STRUGGLE GA (MOSGA) 105 2.3.10 ORTHOGONAL MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM (OMOEA) 106 2.3.11 GENERAL MULTIOBJECTIVE EVOLUTIONARY ALGORITHM (GENMOP) 108 2.3.12 CRITICISM TO PARETO SAMPLING TECHNIQUES 111 2.4 CONSTRAINT-HANDLING TECHNIQUES 113 2.5 CRITICAL MOEA ELEMENTS 116 2.5.1 MOEA COMPARISONS 116 2.5.2 MOEA THEORY 116 2.5.3 MOEA FITNESS FUNCTIONS 117 2.5.4 MOEA CHROMOSOMAL REPRESENTATIONS 117 2.5.5 MOEA PROBLEM DOMAINS 119 2.6 MOEA DESIGN RECAPITULATION 120 2.7 SUMMARY 121 FURTHER EXPLORATIONS 123 3 MOEA LOCAL SEARCH AND COEVOLUTION 131 3.1 INTRODUCTION 131 3.2 MOEA LOCAL SEARCH TECHNIQUES 131 3.2.1 HYBRID MOEA TECHNIQUES 134 3.2.2 COMMENTS ON HYBRID MOEA TECHNIQUES 143 3.3 MOEA COEVOLUTIONARY TECHNIQUES 144 3.4 COEVOLUTION AND SYMBIOSIS IN EAS 147 3.4.1 COEVOLUTIONARY ALGORITHMS 147 3.4.2 COOPERATIVE COEVOLUTIONARY GENETIC ALGORITHMS 149 3.4.3 SYMBIOGENETIC COEVOLUTION 150 3.5 COEVOLUTION AND SYMBIOSIS IN MOEAS 152 3.5.1 ELITIST RECOMBINATIVE MOGA WITH COEVOLUTIONARY SHARING 152 3.5.2 PARMEE S CO-EVOLUTIONARY MOEA 154 CONTENTS XVII 3.5.3 GENETIC SYMBIOSIS ALGORITHM 155 3.5.4 INTERACTIVE GA WITH CO-EVOLVING WEIGHTING FACTORS .... 157 3.5.5 MULTIOBJECTIVE CO-OPERATIVE CO-EVOLUTIONARY GA 158 3.5.6 LOHN S COEVOLUTIONARY GENETIC ALGORITHM 159 3.5.7 DISTRIBUTED COOPERATIVE COEVOLUTIONARY ALGORITHM .... 161 3.5.8 COELLO S COEVOLUTIONARY MOEA 163 3.5.9 NONDOMINATED SORTING COOPERATIVE COEVOLUTIONARY GA 165 3.6 APPLYING COEVOLUTIONARY MOEAS 165 3.6.1 COEVOLVING MULTIPLE MOEAS 166 3.6.2 COEVOLVING MOEAS WITH OTHER SEARCH ALGORITHMS 167 3.6.3 COEVOLVING DENSITY ESTIMATORS 167 3.6.4 COEVOLVING TARGET SOLUTIONS 167 3.6.5 COEVOLVING COMPETING POPULATIONS 168 3.7 FINAL COMMENTS ON COEVOLUTIONARY MOEAS 168 FURTHER EXPLORATIONS 171 4 MOEA TEST SUITES 175 4.1 INTRODUCTION 175 4.2 MOEA TEST FUNCTION SUITE ISSUES 176 4.3 MOP DOMAIN FEATURE CLASSIFICATION 179 4.3.1 UNCONSTRAINED NUMERIC MOEA TEST FUNCTIONS 182 4.3.2 SIDE-CONSTRAINED NUMERIC MOEA TEST FUNCTIONS 187 4.3.3 MOP TEST FUNCTION GENERATORS 193 4.4 GENERIC SCALABLE MOP TEST PROBLEMS 199 4.4.1 OKABE S TEST FUNCTIONS 207 4.4.2 HUBAND S TEST FUNCTIONS 209 4.5 COMBINATORIAL MOEA TEST FUNCTIONS 220 4.6 REAL-WORLD MOEA TEST FUNCTIONS. 222 4.7 SUMMARY 228 FURTHER EXPLORATIONS 229 5 MOEA TESTING AND ANALYSIS 233 5.1 INTRODUCTION 233 5.2 MOEA EXPERIMENTS: MOTIVATION AND OBJECTIVES 235 5.3 EXPERIMENTAL METHODOLOGY 236 5.3.1 MOP PARETO FRONT DETERMINATION 236 5.3.2 MOEA ALGORITHMS TESTING 238 5.3.3 KEY MOEA ALGORITHMIC PARAMETERS 239 5.4 MOEA EXPERIMENTAL MEASUREMENTS 243 5.4.1 SELECTION OF MOEA COMPARISON MEASURES 245 5.4.2 GENERIC ATTAINMENT FUNCTION 245 5.4.3 DOMINANCE RELATIONS 250 5.4.4 PRIMARY QUALITY INDICATORS 254 XVIII CONTENTS 5.4.5 OTHER MOEA QUALITY INDICATORS 263 5.4.6 MOEA EXPERIMENTAL METRICS SUMMARY 267 5.5 MOEA STATISTICAL TESTING APPROACHES 268 5.5.1 STATISTICAL TESTING TECHNIQUES 268 5.5.2 NON-PARAMETRIC STATISTICS (ANALYSIS OF VARIANCE) 270 5.5.3 METHODS FOR PRESENTATION OF MOEA RESULTS 272 5.5.4 VISUALIZATION OF TEST RESULTS 272 5.6 SOFTWARE SUPPORT OF MOEA TESTING 273 5.7 SUMMARY 276 FURTHER EXPLORATIONS 277 6 MOEA THEORY AND ISSUES 283 6.1 INTRODUCTION 283 6.2 PARETO-RELATED THEORETICAL CONTRIBUTIONS 284 6.2.1 PARTIALLY ORDERED SETS 284 6.2.2 MOEA CONVERGENCE 288 6.3 MOEA THEORETICAL ISSUES 300 6.3.1 FITNESS LANDSCAPES 300 6.3.2 FITNESS FUNCTIONS 305 6.3.3 PARETO RANKING 307 6.3.4 PARETO NICHING AND FITNESS SHARING 310 6.3.5 RECOMBINATION OPERATORS 314 6.3.6 MATING RESTRICTIONS 315 6.3.7 SOLUTION STABILITY AND ROBUSTNESS 317 6.3.8 MOEA COMPLEXITY 317 6.3.9 MOEA SCALABILITY 319 6.3.10 RUNNING TIME ANALYSIS 320 6.3.11 MOEA COMPUTATIONAL COST 326 6.3.12 NFL-THEOREM FOR MULTIOBJECTIVE OPTIMIZATION ALGORITHMS 326 6.3.13 ALTERNATIVE DEFMITIONS OF OPTIMALITY 327 6.3.14 LOCAL SEARCH 329 6.4 SUMMARY 333 FURTHER EXPLORATIONS 335 7 APPLICATIONS 339 7.1 INTRODUCTION 339 7.2 ENGINEERING APPLICATIONS 340 7.2.1 ENVIRONMENTAL, NAVAL AND HYDRAULIC ENGINEERING 340 7.2.2 ELECTRICAL AND ELECTRONICS ENGINEERING 347 7.2.3 TELECONIMUNICATIONS AND NETWORK OPTIMIZATION 356 7.2.4 ROBOTICS AND CONTROL ENGINEERING 360 7.2.5 STRUCTURAL AND MECHANICAL ENGINEERING 369 CONTENTS XIX 7.2.6 CIVIL AND CONSTRUCTION ENGINEERING 376 7.2.7 TRANSPORT ENGINEERING 377 7.2.8 AERONAUTICAL ENGINEERING 381 7.3 SCIENTIFIC APPLICATIONS 388 7.3.1 GEOGRAPHY 388 7.3.2 CHEMISTRY 389 7.3.3 PHYSICS 391 7.3.4 MEDICINE 393 7.3.5 ECOLOGY 396 7.3.6 COMPUTER SCIENCE AND COMPUTER ENGINEERING 397 7.4 INDUSTRIAL APPLICATIONS 407 7.4.1 DESIGN AND MANUFACTURE 408 7.4.2 SCHEDULING 416 7.4.3 MANAGEMENT 424 7.4.4 GROUPING AND PACKING 426 7.5 MISCELLANEOUS APPLICATIONS 428 7.5.1 FINANCE 428 7.5.2 CLASSIFICATION AND PREDICTION 430 7.6 FUTURE APPLICATIONS 434 7.7 SUMMARY 435 FURTHER EXPLORATIONS 437 8 MOEA PARALLELIZATION 443 8.1 INTRODUCTION 443 8.2 PMOEA FUNDAMENTAL BACKGROUND 445 8.2.1 PMOEA NOTATION 445 8.2.2 PMOEA MOTIVATION AND ISSUES 446 8.3 PMOEA PARADIGMS 450 8.3.1 MASTER-SLAVE PMOEA MODEL 452 8.3.2 ISLAND PMOEA MODELS 455 8.3.3 DIFFUSION PMOEA MODEL 458 8.3.4 HIERARCHICAL HYBRID PMOEA MODELS 459 8.4 PMOEAS FROM THE LITERATURE 460 8.4.1 MASTER-SLAVE PMOEAS 460 8.4.2 ISLAND PMOEAS 465 8.4.3 DIFFUSION PMOEAS 473 8.5 PMOEA ANALYSES AND ISSUES 475 8.5.1 PMOEA OBSERVATIONS 476 8.5.2 PMOEA SUITABILITY ISSUES 476 8.5.3 PMOEA HARDWARE AND SOFTWARE ARCHITECTURE ISSUES . . . 477 8.5.4 PMOEA TEST FUNCTION ISSUES 480 8.5.5 PMOEA METRIC/PARAMETER ISSUES 484 8.6 PMOEA DEVELOPMENT ISSUES 488 8.6.1 PMOEA CREATION OPTIONS 490 XX CONTENTS 8.6.2 MASTER-SLAVE IMPLEMENTATION ISSUES 491 8.6.3 ISLAND IMPLEMENTATION ISSUES 493 8.6.4 DIFFUSION IMPLEMENTATION ISSUES 499 8.6.5 PARALLEL NICHING ISSUES 500 8.6.6 PARALLEL ARCHIVING ISSUES 502 8.6.7 PMOEA THEORY ISSUES 503 8.7 A GENERIC PMOEA 503 8.7.1 ENGINEERING A PMOEA 504 8.7.2 GENERICIZING A PMOEA 507 8.8 CONCLUSIONS 507 FURTHER EXPLORATIONS 509 9 MULTI-CRITERIA DECISION MAKING 515 9.1 INTRODUCTION 515 9.2 MULTI-CRITERIA DECISION MAKING 516 9.2.1 OPERATIONAL ATTITUEDE OF THE DECISION MAKER 517 9.2.2 WHEN TO GET THE PREFERENCE INFORMATION? 518 9.3 INCORPORATION OF PREFERENCES IN MOEAS 520 9.3.1 DEFINITION OF DESIRED GOALS 522 9.3.2 UTILITY FUNCTIONS 526 9.3.3 PREFERENCE RELATIONS 528 9.3.4 OUTRANKING 531 9.3.5 FUZZY LOGIC 533 9.3.6 COMPROMISE PROGRAMMING 535 9.4 ISSUES DESERVING ATTENTION 536 9.4.1 PRESERVING DOMINANCE 537 9.4.2 TRANSITIVITY 537 9.4.3 SCALABILITY 537 9.4.4 GROUP DECISION MAKING 537 9.4.5 OTHER IMPORTANT ISSUES 539 9.5 SUMMARY 540 FURTHER EXPLORATIONS 541 10 ALTERNATIVE METAHEURISTICS 54 7 10.1 INTRODUCTION 547 10.2 SIMULATED ANNEALING 548 10.2.1 BASIC CONCEPTS 548 10.2.2 MULTIOBJECTIVE VERSIONS 550 10.2.3 ADVANTAGES AND DISADVANTAGES OF SIMULATED ANNEALING . 556 10.3 TABU SEARCH AND SCATTER SEARCH 557 10.3.1 BASIC CONCEPTS 558 10.3.2 MULTIOBJECTIVE VERSIONS 559 CONTENTS XXI 10.3.3 ADVANTAGES AND DISADVANTAGES OF TABU SEARCH AND SCATTER SEARCH 571 10.4 ANT SYSTEM : 572 10.4.1 BASIC CONCEPTS 572 10.4.2 MULTIOBJECTIVE VERSIONS 575 10.4.3 ADVANTAGES AND DISADVANTAGES OF THE ANT SYSTEM 581 10.5 DISTRIBUTED REINFORCEMENT LEARNING 582 10.5.1 BASIC CONCEPTS 582 10.5.2 ADVANTAGES AND DISADVANTAGES OF DISTRIBUTED REINFORCEMENT LEARNING 583 10.6 PARTICLE SWARM OPTIMIZATION 584 10.6.1 BASIC CONCEPTS 584 10.6.2 MULTIOBJECTIVE VERSIONS 585 10.6.3 ADVANTAGES AND DISADVANTAGES OF PARTICLE SWARM OPTIMIZATION 593 10.7 DIFFERENTIAL EVOLUTION 594 10.7.1 MULTIOBJECTIVE VERSIONS 596 10.7.2 ADVANTAGES AND DISADVANTAGES OF DIFFERENTIAL EVOLUTION 604 10.8 ARTIFICIAL IMMUNE SYSTEMS 604 10.8.1 BASIC CONCEPTS 605 10.8.2 MULTIOBJECTIVE VERSIONS 606 10.8.3 ADVANTAGES AND DISADVANTAGES OF ARTIFICIAL IMMUNE SYSTEMS 611 10.9 OTHER HEURISTICS 612 10.9.1 CULTURAL ALGORITHMS 612 10.9.2 COOPERATIVE SEARCH 614 LO.LOSUMMARY 616 FURTHER EXPLORATIONS 617 EPILOG 623 REFERENCES 627 INDEX 761
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author	Coello Coello, Carlos A. 1967- Lamont, Gary B. Van Veldhuizen, David A.
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owner	DE-703 DE-29T DE-384 DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-11
owner_facet	DE-703 DE-29T DE-384 DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-11
physical	XXI, 800 S. graph. Darst.
publishDate	2007
publishDateSearch	2007
publishDateSort	2007
publisher	Springer
record_format	marc
series2	Genetic and evolutionary computation series
spellingShingle	Coello Coello, Carlos A. 1967- Lamont, Gary B. Van Veldhuizen, David A. Evolutionary algorithms for solving multi-objective problems Evolutionärer Algorithmus (DE-588)4366912-8 gnd Mehrkriterielle Optimierung (DE-588)4610682-0 gnd Evolutionäre Systementwicklung (DE-588)4672442-4 gnd
subject_GND	(DE-588)4366912-8 (DE-588)4610682-0 (DE-588)4672442-4
title	Evolutionary algorithms for solving multi-objective problems
title_auth	Evolutionary algorithms for solving multi-objective problems
title_exact_search	Evolutionary algorithms for solving multi-objective problems
title_full	Evolutionary algorithms for solving multi-objective problems Carlos A. Coello Coello ; Gary B. Lamont ; David A. van Veldhuizen
title_fullStr	Evolutionary algorithms for solving multi-objective problems Carlos A. Coello Coello ; Gary B. Lamont ; David A. van Veldhuizen
title_full_unstemmed	Evolutionary algorithms for solving multi-objective problems Carlos A. Coello Coello ; Gary B. Lamont ; David A. van Veldhuizen
title_short	Evolutionary algorithms for solving multi-objective problems
title_sort	evolutionary algorithms for solving multi objective problems
topic	Evolutionärer Algorithmus (DE-588)4366912-8 gnd Mehrkriterielle Optimierung (DE-588)4610682-0 gnd Evolutionäre Systementwicklung (DE-588)4672442-4 gnd
topic_facet	Evolutionärer Algorithmus Mehrkriterielle Optimierung Evolutionäre Systementwicklung
url	http://deposit.dnb.de/cgi-bin/dokserv?id=2813140&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=015862047&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT coellocoellocarlosa evolutionaryalgorithmsforsolvingmultiobjectiveproblems AT lamontgaryb evolutionaryalgorithmsforsolvingmultiobjectiveproblems AT vanveldhuizendavida evolutionaryalgorithmsforsolvingmultiobjectiveproblems

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