Reactive search and intelligent optimization:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer
2008
|
| Ausgabe: | [1. ed.] |
| Schriftenreihe: | Operations research, computer science interfaces series
45 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | X, 196 S. Ill., graph. Darst. |
| ISBN: | 9780387096230 9780387096247 |
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Datensatz im Suchindex
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| adam_text | ROBERTO BATTITI * MAURO BRUNATO * FRANCO MASCIA REACTIVE SEARCH AND
INTELLIGENT OPTIMIZATION 4Y SPRINGER CONTENTS 1 INTRODUCTION: MACHINE
LEARNING FOR INTELLIGENT OPTIMIZATION 1 1.1 PARAMETER TUNING AND
INTELLIGENT OPTIMIZATION 4 1.2 BOOK OUTLINE 7 2 REACTING ON THE
NEIGHBORHOOD 9 2.1 LOCAL SEARCH BASED ON PERTURBATIONS 9 2.2 LEARNING
HOW TO EVALUATE THE NEIGHBORHOOD 13 2.3 LEARNING THE APPROPRIATE
NEIGHBORHOOD IN VARIABLE NEIGHBORHOOD SEARCH 14 2.4 ITERATED LOCAL
SEARCH 18 3 REACTING ON THE ANNEALING SCHEDULE 25 3.1 STOCHASTICITY IN
LOCAL MOVES AND CONTROLLED WORSENING OF SOLUTION VALUES 25 3.2 SIMULATED
ANNEALING AND ASYMPTOTICS 26 3.2.1 ASYMPTOTIC CONVERGENCE RESULTS 27 3.3
ONLINE LEARNING STRATEGIES IN SIMULATED ANNEALING 29 3.3.1 COMBINATORIAL
OPTIMIZATION PROBLEMS 30 3.3.2 GLOBAL OPTIMIZATION OF CONTINUOUS
FUNCTIONS 33 4 REACTIVE PROHIBITIONS 35 4.1 PROHIBITIONS FOR
DIVERSIFICATION 35 4.1.1 FORMS OF PROHIBITION-BASED SEARCH 36 4.1.2
DYNAMICAL SYSTEMS 37 4.1.3 A WORKED-OUT EXAMPLE OF FIXED TABU SEARCH 39
4.1.4 RELATIONSHIP BETWEEN PROHIBITION AND DIVERSIFICATION 39 4.1.5 HOW
TO ESCAPE FROM AN ATTRACTOR 41 4.2 REACTIVE TABU SEARCH: SELF-ADJUSTED
PROHIBITION PERIOD 49 4.2.1 THE ESCAPE MECHANISM 51 4.2.2 APPLICATIONS
OF REACTIVE TABU SEARCH 51 V VI CONTENTS 4.3 IMPLEMENTATION: STORING AND
USING THE SEARCH HISTORY 52 4.3.1 FAST ALGORITHMS FOR USING THE SEARCH
HISTORY 54 4.3.2 PERSISTENT DYNAMIC SETS 54 5 REACTING ON THE OBJECTIVE
FUNCTION 59 5.1 DYNAMIC LANDSCAPE MODIFICATIONS TO INFLUENCE
TRAJECTORIES 59 5.1.1 ADAPTING NOISE LEVELS 62 5.1.2 GUIDED LOCAL SEARCH
63 5.2 ELIMINATING PLATEAUS BY LOOKING INSIDE THE PROBLEM STRUCTURE 66
5.2.1 NONOBLIVIOUS LOCAL SEARCH FOR SAT 66 6 MODEL-BASED SEARCH 69 6. 1
MODELS OF A PROBLEM 69 6.2 AN EXAMPLE 71 6.3 DEPENDENT PROBABILITIES 73
6.4 THE CROSS-ENTROPY MODEL 75 6.5 ADAPTIVE SOLUTION CONSTRUCTION WITH
ANT COLONIES 77 6.6 MODELING SURFACES FOR CONTINUOUS OPTIMIZATION 79 7
SUPERVISED LEARNING 83 7.1 LEARNING TO OPTIMIZE, FROM EXAMPLES 83 7.2
TECHNIQUES 84 7.2.1 LINEAR REGRESSION 84 7.2.2 BAYESIAN LOCALLY WEIGHTED
REGRESSION 88 7.2.3 USING LINEAR FUNCTIONS FOR CLASSIFICATION 92 7.2.4
MULTILAYER PERCEPTRONS 94 7.2.5 STATISTICAL LEARNING THEORY AND SUPPORT
VECTOR MACHINES .. 95 7.2.6 NEAREST NEIGHBOR S METHODS 101 7.3 SELECTING
FEATURES 102 7.3.1 CORRELATION COEFFICIENT 104 7.3.2 CORRELATION RATIO
104 7.3.3 ENTROPY AND MUTUAL INFORMATION 105 7.4 APPLICATIONS 106 7.4.1
LEARNING A MODEL OF THE SOLVER 110 8 REINFORCEMENT LEARNING 117 8.1
REINFORCEMENT LEARNING BASICS: LEARNING FROM A CRITIC 117 8.1.1 MARKOV
DECISION PROCESSES 118 8.1.2 DYNAMIC PROGRAMMING 120 8.1.3
APPROXIMATIONS: REINFORCEMENT LEARNING AND NEURO-DYNAMIC PROGRAMMING 123
8.2 RELATIONSHIPS BETWEEN REINFORCEMENT LEARNING AND OPTIMIZATION 125
CONTENTS VII 9 ALGORITHM PORTFOLIOS AND RESTART STRATEGIES 1 29 9. 1
INTRODUCTION: PORTFOLIOS AND RESTARTS 129 9.2 PREDICTING THE PERFORMANCE
OF A PORTFOLIO FROM ITS COMPONENT ALGORITHMS 130 9.2.1 PARALLEL
PROCESSING 132 9.3 REACTIVE PORTFOLIOS 134 9.4 DEFINING AN OPTIMAL
RESTART TIME 135 9.5 REACTIVE RESTARTS 138 10 RACING 141 10.1
EXPLORATION AND EXPLOITATION OF CANDIDATE ALGORITHMS 141 10.2 RACING TO
MAXIMIZE CUMULATIVE REWARD BY INTERVAL ESTIMATION . .. 142 10.3 AIMING
AT THE MAXIMUM WITH THRESHOLD ASCENT 144 10.4 RACING FOR OFF-LINE
CONFIGURATION OF METAHEURISTICS 145 11 TEAMS OF INTERACTING SOLVERS 151
11.1 COMPLEX INTERACTION AND COORDINATION SCHEMES 151 11.2 GENETIC
ALGORITHMS AND EVOLUTION STRATEGIES 152 11.3 INTELLIGENT AND REACTIVE
SOLVER TEAMS 156 11.4 AN EXAMPLE: GOSSIPING OPTIMIZATION 159 11.4.1
EPIDEMIC COMMUNICATION FOR OPTIMIZATION 160 12 METRICS, LANDSCAPES, AND
FEATURES 163 12.1 HOW TO MEASURE AND MODEL PROBLEM DIFFICULTY 163 12.2
PHASE TRANSITIONS IN COMBINATORIAL PROBLEMS 164 12.3 EMPIRICAL MODELS
FOR FITNESS SURFACES 165 12.3.1 TUNABLE LANDSCAPES 168 12.4 MEASURING
LOCAL SEARCH COMPONENTS: DIVERSIFICATION AND BIAS .... 170 12.4.1 THE
DIVERSIFICATION-BIAS COMPROMISE (D-B PLOTS) 173 12.4.2 A CONJECTURE:
BETTER ALGORITHMS ARE PARETO-OPTIMAL IN D-B PLOTS 175 13 OPEN PROBLEMS
177 REFERENCES 181 INDEX 195
|
| adam_txt |
ROBERTO BATTITI * MAURO BRUNATO * FRANCO MASCIA REACTIVE SEARCH AND
INTELLIGENT OPTIMIZATION 4Y SPRINGER CONTENTS 1 INTRODUCTION: MACHINE
LEARNING FOR INTELLIGENT OPTIMIZATION 1 1.1 PARAMETER TUNING AND
INTELLIGENT OPTIMIZATION 4 1.2 BOOK OUTLINE 7 2 REACTING ON THE
NEIGHBORHOOD 9 2.1 LOCAL SEARCH BASED ON PERTURBATIONS 9 2.2 LEARNING
HOW TO EVALUATE THE NEIGHBORHOOD 13 2.3 LEARNING THE APPROPRIATE
NEIGHBORHOOD IN VARIABLE NEIGHBORHOOD SEARCH 14 2.4 ITERATED LOCAL
SEARCH 18 3 REACTING ON THE ANNEALING SCHEDULE 25 3.1 STOCHASTICITY IN
LOCAL MOVES AND CONTROLLED WORSENING OF SOLUTION VALUES 25 3.2 SIMULATED
ANNEALING AND ASYMPTOTICS 26 3.2.1 ASYMPTOTIC CONVERGENCE RESULTS 27 3.3
ONLINE LEARNING STRATEGIES IN SIMULATED ANNEALING 29 3.3.1 COMBINATORIAL
OPTIMIZATION PROBLEMS 30 3.3.2 GLOBAL OPTIMIZATION OF CONTINUOUS
FUNCTIONS 33 4 REACTIVE PROHIBITIONS 35 4.1 PROHIBITIONS FOR
DIVERSIFICATION 35 4.1.1 FORMS OF PROHIBITION-BASED SEARCH 36 4.1.2
DYNAMICAL SYSTEMS 37 4.1.3 A WORKED-OUT EXAMPLE OF FIXED TABU SEARCH 39
4.1.4 RELATIONSHIP BETWEEN PROHIBITION AND DIVERSIFICATION 39 4.1.5 HOW
TO ESCAPE FROM AN ATTRACTOR 41 4.2 REACTIVE TABU SEARCH: SELF-ADJUSTED
PROHIBITION PERIOD 49 4.2.1 THE ESCAPE MECHANISM 51 4.2.2 APPLICATIONS
OF REACTIVE TABU SEARCH 51 V VI CONTENTS 4.3 IMPLEMENTATION: STORING AND
USING THE SEARCH HISTORY 52 4.3.1 FAST ALGORITHMS FOR USING THE SEARCH
HISTORY 54 4.3.2 PERSISTENT DYNAMIC SETS 54 5 REACTING ON THE OBJECTIVE
FUNCTION 59 5.1 DYNAMIC LANDSCAPE MODIFICATIONS TO INFLUENCE
TRAJECTORIES 59 5.1.1 ADAPTING NOISE LEVELS 62 5.1.2 GUIDED LOCAL SEARCH
63 5.2 ELIMINATING PLATEAUS BY LOOKING INSIDE THE PROBLEM STRUCTURE 66
5.2.1 NONOBLIVIOUS LOCAL SEARCH FOR SAT 66 6 MODEL-BASED SEARCH 69 6. 1
MODELS OF A PROBLEM 69 6.2 AN EXAMPLE 71 6.3 DEPENDENT PROBABILITIES 73
6.4 THE CROSS-ENTROPY MODEL 75 6.5 ADAPTIVE SOLUTION CONSTRUCTION WITH
ANT COLONIES 77 6.6 MODELING SURFACES FOR CONTINUOUS OPTIMIZATION 79 7
SUPERVISED LEARNING 83 7.1 LEARNING TO OPTIMIZE, FROM EXAMPLES 83 7.2
TECHNIQUES 84 7.2.1 LINEAR REGRESSION 84 7.2.2 BAYESIAN LOCALLY WEIGHTED
REGRESSION 88 7.2.3 USING LINEAR FUNCTIONS FOR CLASSIFICATION 92 7.2.4
MULTILAYER PERCEPTRONS 94 7.2.5 STATISTICAL LEARNING THEORY AND SUPPORT
VECTOR MACHINES . 95 7.2.6 NEAREST NEIGHBOR'S METHODS 101 7.3 SELECTING
FEATURES 102 7.3.1 CORRELATION COEFFICIENT 104 7.3.2 CORRELATION RATIO
104 7.3.3 ENTROPY AND MUTUAL INFORMATION 105 7.4 APPLICATIONS 106 7.4.1
LEARNING A MODEL OF THE SOLVER 110 8 REINFORCEMENT LEARNING 117 8.1
REINFORCEMENT LEARNING BASICS: LEARNING FROM A CRITIC 117 8.1.1 MARKOV
DECISION PROCESSES 118 8.1.2 DYNAMIC PROGRAMMING 120 8.1.3
APPROXIMATIONS: REINFORCEMENT LEARNING AND NEURO-DYNAMIC PROGRAMMING 123
8.2 RELATIONSHIPS BETWEEN REINFORCEMENT LEARNING AND OPTIMIZATION 125
CONTENTS VII 9 ALGORITHM PORTFOLIOS AND RESTART STRATEGIES 1 29 9. 1
INTRODUCTION: PORTFOLIOS AND RESTARTS 129 9.2 PREDICTING THE PERFORMANCE
OF A PORTFOLIO FROM ITS COMPONENT ALGORITHMS 130 9.2.1 PARALLEL
PROCESSING 132 9.3 REACTIVE PORTFOLIOS 134 9.4 DEFINING AN OPTIMAL
RESTART TIME 135 9.5 REACTIVE RESTARTS 138 10 RACING 141 10.1
EXPLORATION AND EXPLOITATION OF CANDIDATE ALGORITHMS 141 10.2 RACING TO
MAXIMIZE CUMULATIVE REWARD BY INTERVAL ESTIMATION . . 142 10.3 AIMING
AT THE MAXIMUM WITH THRESHOLD ASCENT 144 10.4 RACING FOR OFF-LINE
CONFIGURATION OF METAHEURISTICS 145 11 TEAMS OF INTERACTING SOLVERS 151
11.1 COMPLEX INTERACTION AND COORDINATION SCHEMES 151 11.2 GENETIC
ALGORITHMS AND EVOLUTION STRATEGIES 152 11.3 INTELLIGENT AND REACTIVE
SOLVER TEAMS 156 11.4 AN EXAMPLE: GOSSIPING OPTIMIZATION 159 11.4.1
EPIDEMIC COMMUNICATION FOR OPTIMIZATION 160 12 METRICS, LANDSCAPES, AND
FEATURES 163 12.1 HOW TO MEASURE AND MODEL PROBLEM DIFFICULTY 163 12.2
PHASE TRANSITIONS IN COMBINATORIAL PROBLEMS 164 12.3 EMPIRICAL MODELS
FOR FITNESS SURFACES 165 12.3.1 TUNABLE LANDSCAPES 168 12.4 MEASURING
LOCAL SEARCH COMPONENTS: DIVERSIFICATION AND BIAS . 170 12.4.1 THE
DIVERSIFICATION-BIAS COMPROMISE (D-B PLOTS) 173 12.4.2 A CONJECTURE:
BETTER ALGORITHMS ARE PARETO-OPTIMAL IN D-B PLOTS 175 13 OPEN PROBLEMS
177 REFERENCES 181 INDEX 195 |
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| illustrated | Illustrated |
| index_date | 2024-07-02T22:02:37Z |
| indexdate | 2024-07-09T21:21:26Z |
| institution | BVB |
| isbn | 9780387096230 9780387096247 |
| language | English |
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| physical | X, 196 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| record_format | marc |
| series | Operations research, computer science interfaces series |
| series2 | Operations research, computer science interfaces series |
| spelling | Battiti, Roberto Verfasser aut Reactive search and intelligent optimization Roberto Battiti ; Mauro Brunato ; Franco Mascia [1. ed.] New York, NY Springer 2008 X, 196 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Operations research, computer science interfaces series 45 Combinatorial optimization Heuristic programming Problem solving Optimierung (DE-588)4043664-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Optimierung (DE-588)4043664-0 s DE-604 Brunato, Mauro Verfasser aut Mascia, Franco Verfasser aut Operations research, computer science interfaces series 45 (DE-604)BV012124389 45 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016735280&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Battiti, Roberto Brunato, Mauro Mascia, Franco Reactive search and intelligent optimization Operations research, computer science interfaces series Combinatorial optimization Heuristic programming Problem solving Optimierung (DE-588)4043664-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4043664-0 (DE-588)4193754-5 |
| title | Reactive search and intelligent optimization |
| title_auth | Reactive search and intelligent optimization |
| title_exact_search | Reactive search and intelligent optimization |
| title_exact_search_txtP | Reactive search and intelligent optimization |
| title_full | Reactive search and intelligent optimization Roberto Battiti ; Mauro Brunato ; Franco Mascia |
| title_fullStr | Reactive search and intelligent optimization Roberto Battiti ; Mauro Brunato ; Franco Mascia |
| title_full_unstemmed | Reactive search and intelligent optimization Roberto Battiti ; Mauro Brunato ; Franco Mascia |
| title_short | Reactive search and intelligent optimization |
| title_sort | reactive search and intelligent optimization |
| topic | Combinatorial optimization Heuristic programming Problem solving Optimierung (DE-588)4043664-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Combinatorial optimization Heuristic programming Problem solving Optimierung Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016735280&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV012124389 |
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