Representations for genetic and evolutionary algorithms:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2006
|
| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 295 - 314 |
| Beschreibung: | XVII, 325 S. graph. Darst. |
| ISBN: | 9783540250593 354025059X |
Internformat
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| 245 | 1 | 0 | |a Representations for genetic and evolutionary algorithms |c Franz Rothlauf |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2006 | |
| 300 | |a XVII, 325 S. |b graph. Darst. | ||
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| 500 | |a Literaturverz. S. 295 - 314 | ||
| 650 | 4 | |a Algorithmes génétiques | |
| 650 | 7 | |a Algoritmos genéticos |2 larpcal | |
| 650 | 7 | |a Computação evolutiva |2 larpcal | |
| 650 | 4 | |a Programmation génétique (Informatique) | |
| 650 | 4 | |a Programmation évolutive | |
| 650 | 7 | |a Representação de grupos |2 larpcal | |
| 650 | 4 | |a Représentations d'algèbres | |
| 650 | 4 | |a Représentations de groupes | |
| 650 | 4 | |a Evolutionary programming (Computer science) | |
| 650 | 4 | |a Genetic algorithms | |
| 650 | 4 | |a Genetic programming (Computer science) | |
| 650 | 4 | |a Representations of algebras | |
| 650 | 4 | |a Representations of groups | |
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Datensatz im Suchindex
| _version_ | 1805083694969389056 |
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| adam_text |
FRANZ ROTHLAUF REPRESENTATIONS FOR GENETIC AND EVOLUTIONARY ALGORITHMS
FYA SPRINGER CONTENTS 1 INTRODUCTION 1 1.1 PURPOSE 2 1.2 ORGANIZATION 4
2 REPRESENTATIONS FOR GENETIC AND EVOLUTIONARY ALGORITHMS . 9 2.1
GENETIC REPRESENTATIONS 10 2.1.1 GENOTYPES AND PHENOTYPES 10 2.1.2
DECOMPOSITION OF THE FITNESS FUNCTION 11 2.1.3 TYPES OF REPRESENTATIONS
13 2.2 GENETIC AND EVOLUTIONARY ALGORITHMS 15 2.2.1 PRINCIPLES 15 2.2.2
FUNCTIONALITY 16 2.2.3 SCHEMA THEOREM AND BUILDING BLOCK HYPOTHESIS 18
2.3 PROBLEM DIFFICULTY 22 2.3.1 REASONS FOR PROBLEM DIFFICULTY 22 2.3.2
MEASUREMENTS OF PROBLEM DIFFICULTY 25 2.4 EXISTING RECOMMENDATIONS FOR
THE DESIGN OF EFFICIENT REPRESENTATIONS 28 2.4.1 GOLDBERG'S MEANINGFUL
BUILDING BLOCKS AND MINIMAL ALPHABETS 28 2.4.2 RADCLIFFE'S FORMAE AND
EQUIVALENCE CLASSES 29 2.4.3 PALMER'S TREE ENCODING ISSUES 31 2.4.4
RONALD'S REPRESENTATIONAL REDUNDANCY 31 3 THREE ELEMENTS OF A THEORY OF
REPRESENTATIONS 33 3.1 REDUNDANCY 35 3.1.1 REDUNDANT REPRESENTATIONS AND
NEUTRAL NETWORKS 35 3.1.2 SYNONYMOUSLY AND NON-SYNONYMOUSLY REDUNDANT
REPRESENTATIONS 38 3.1.3 COMPLEXITY MODEL FOR REDUNDANT REPRESENTATIONS
45 XIV CONTENTS 3.1.4 POPULATION SIZING FOR SYNONYMOUSLY REDUNDANT
REPRESENTATIONS 47 3.1.5 RUN DURATION AND OVERALL PROBLEM COMPLEXITY FOR
SYNONYMOUSLY REDUNDANT REPRESENTATIONS 49 3.1.6 ANALYZING THE REDUNDANT
TRIVIAL VOTING MAPPING 50 3.1.7 CONCLUSIONS AND FURTHER RESEARCH 57 3.2
SCALING 59 3.2.1 DEFINITIONS AND BACKGROUND 59 3.2.2 POPULATION SIZING
MODEL FOR EXPONENTIALLY SCALED REPRESENTATIONS NEGLECTING THE EFFECT OF
GENETIC DRIFT . 61 3.2.3 POPULATION SIZING MODEL FOR EXPONENTIALLY
SCALED REPRESENTATIONS CONSIDERING THE EFFECT OF GENETIC DRIFT 65 3.2.4
EMPIRICAL RESULTS FOR BINLNT PROBLEMS 68 3.2.5 CONCLUSIONS 72 3.3
LOCALITY 73 3.3.1 INFLUENCE OF REPRESENTATIONS ON PROBLEM DIFFICULTY 74
3.3.2 METRICS, LOCALITY, AND MUTATION OPERATORS 76 3.3.3
PHENOTYPE-FITNESS MAPPINGS AND PROBLEM DIFFICULTY . 78 3.3.4 INFLUENCE
OF LOCALITY ON PROBLEM DIFFICULTY 81 3.3.5 DISTANCE DISTORTION AND
CROSSOVER OPERATORS 84 3.3.6 MODIFYING BB-COMPLEXITY FOR THE ONE-MAX
PROBLEM . . 86 3.3.7 EMPIRICAL RESULTS 89 3.3.8 CONCLUSIONS 93 3.4
SUMMARY AND CONCLUSIONS 95 4 TIME-QUALITY FRAMEWORK FOR A THEORY-BASED
ANALYSIS AND DESIGN OF REPRESENTATIONS 97 4.1 SOLUTION QUALITY AND TIME
TO CONVERGENCE 98 4.2 ELEMENTS OF THE FRAMEWORK 99 4.2.1 REDUNDANCY 99
4.2.2 SCALING 100 4.2.3 LOCALITY 101 4.3 THE FRAMEWORK 102 4.3.1
UNIFORMLY SCALED REPRESENTATIONS 104 4.3.2 EXPONENTIALLY SCALED
REPRESENTATIONS 105 4.4 IMPLICATIONS FOR THE DESIGN OF REPRESENTATIONS
108 4.4.1 UNIFORMLY REDUNDANT REPRESENTATIONS ARE ROBUST . 108 4.4.2
EXPONENTIALLY SCALED REPRESENTATIONS ARE FAST, BUT INACCURATE ILL 4.4.3
LOW-LOCALITY REPRESENTATIONS ARE DIFFICULT TO PREDICT, AND NO GOOD
CHOICE 112 4.5 SUMMARY AND CONCLUSIONS 114 CONTENTS XV ANALYSIS OF
BINARY REPRESENTATIONS OF INTEGERS 117 5.1 INTEGER OPTIMIZATION PROBLEMS
118 5.2 BINARY STRING REPRESENTATIONS 120 5.3 A THEORETICAL COMPARISON
123 5.3.1 REDUNDANCY AND THE UNARY ENCODING 123 5.3.2 SCALING,
MODIFICATION OF PROBLEM DIFFICULTY, AND THE BINARY ENCODING 126 5.3.3
MODIFICATION OF PROBLEM DIFFICULTY AND THE GRAY ENCODING 127 5.4
EXPERIMENTAL RESULTS 129 5.4.1 INTEGER ONE-MAX PROBLEM AND DECEPTIVE
INTEGER ONE-MAX PROBLEM 129 5.4.2 MODIFICATIONS OF THE INTEGER ONE-MAX
PROBLEM 134 5.5 SUMMARY AND CONCLUSIONS 139 ANALYSIS AND DESIGN OF
REPRESENTATIONS FOR TREES 141 6.1 THE TREE DESIGN PROBLEM 142 6.1.1
DEFINITIONS 142 6.1.2 METRICS AND DISTANCES 144 6.1.3 TREE STRUCTURES
145 6.1.4 SCHEMA ANALYSIS FOR GRAPHS 146 6.1.5 SCALABLE TEST PROBLEMS
FOR GRAPHS 147 6.1.6 TREE ENCODING ISSUES 150 6.2 PRIIFER NUMBERS 151
6.2.1 HISTORICAL REVIEW 152 6.2.2 CONSTRUCTION 154 6.2.3 PROPERTIES 156
6.2.4 THE LOW LOCALITY OF THE PRIIFER NUMBER ENCODING 157 6.2.5 SUMMARY
AND CONCLUSIONS 169 6.3 THE CHARACTERISTIC VECTOR ENCODING 171 6.3.1
ENCODING TREES WITH CHARACTERISTIC VECTORS 171 6.3.2 REPAIRING INVALID
SOLUTIONS 172 6.3.3 BIAS AND NON-SYNONYMOUS REDUNDANCY 173 6.3.4 SUMMARY
177 6.4 THE LINK AND NODE BIASED ENCODING 178 6.4.1 MOTIVATION AND
FUNCTIONALITY 179 6.4.2 BIAS AND NON-UNIFORMLY REDUNDANT
REPRESENTATIONS. . . 183 6.4.3 THE NODE-BIASED ENCODING 184 6.4.4 A
CONCEPT FOR THE ANALYSIS OF REDUNDANT REPRESENTATIONS 187 6.4.5
POPULATION SIZING FOR THE LINK-BIASED ENCODING 191 6.4.6 THE
LINK-AND-NODE-BIASED ENCODING 195 6.4.7 EXPERIMENTAL RESULTS 197 6.4.8
CONCLUSIONS 200 6.5 NETWORK RANDOM KEYS (NETKEYS) 201 XVI CONTENTS 6.5.1
MOTIVATION 202 6.5.2 FUNCTIONALITY 202 6.5.3 PROPERTIES 207 6.5.4
UNIFORM REDUNDANCY 208 6.5.5 POPULATION SIZING AND RUN DURATION FOR THE
ONE-MAX TREE PROBLEM 210 6.5.6 CONCLUSIONS 212 6.6 CONCLUSIONS 213 7
ANALYSIS AND DESIGN OF SEARCH OPERATORS FOR TREES 217 7.1 NETDIR: A
DIRECT REPRESENTATION FOR TREES 218 7.1.1 HISTORICAL REVIEW 218 7.1.2
PROPERTIES OF DIRECT REPRESENTATIONS 219 7.1.3 OPERATORS FOR NETDIR 220
7.1.4 SUMMARY 223 7.2 THE EDGE-SET ENCODING 224 7.2.1 FUNCTIONALITY 225
7.2.2 BIAS 227 7.2.3 PERFORMANCE FOR THE OCST PROBLEM 230 7.2.4 SUMMARY
AND CONCLUSIONS 237 8 PERFORMANCE OF GENETIC AND EVOLUTIONARY ALGORITHMS
ON TREE PROBLEMS 241 8.1 GEA PERFORMANCE ON SCALABLE TEST TREE PROBLEMS
242 8.1.1 ANALYSIS OF REPRESENTATIONS 242 8.1.2 ONE-MAX TREE PROBLEM 246
8.1.3 DECEPTIVE TRAP PROBLEM FOR TREES 251 8.2 GEA PERFORMANCE ON THE
OCST PROBLEM 256 8.2.1 THE OPTIMAL COMMUNICATION SPANNING TREE PROBLEM .
. 257 8.2.2 OPTIMIZATION METHODS FOR THE OPTIMAL COMMUNICATION SPANNING
TREE PROBLEM 258 8.2.3 DESCRIPTION OF TEST PROBLEMS 260 8.2.4 ANALYSIS
OF REPRESENTATIONS 262 8.2.5 THEORETICAL PREDICTIONS ON THE PERFORMANCE
OF REPRESENTATIONS 264 8.2.6 EXPERIMENTAL RESULTS 266 8.3 SUMMARY 272 9
SUMMARY AND CONCLUSIONS 275 9.1 SUMMARY 275 9.2 CONCLUSIONS 277 CONTENTS
XVII A OPTIMAL COMMUNICATION SPANNING TREE TEST INSTANCES . 281 A.I
PALMER'S TEST INSTANCES 281 A.2 RAIDL'S TEST INSTANCES 285 A.3 BERRY'S
TEST INSTANCES 289 A.4 REAL WORLD PROBLEMS 291 LIST OF SYMBOLS 315 LIST
OF ACRONYMS 319 INDEX 321 |
| adam_txt |
FRANZ ROTHLAUF REPRESENTATIONS FOR GENETIC AND EVOLUTIONARY ALGORITHMS
FYA SPRINGER CONTENTS 1 INTRODUCTION 1 1.1 PURPOSE 2 1.2 ORGANIZATION 4
2 REPRESENTATIONS FOR GENETIC AND EVOLUTIONARY ALGORITHMS . 9 2.1
GENETIC REPRESENTATIONS 10 2.1.1 GENOTYPES AND PHENOTYPES 10 2.1.2
DECOMPOSITION OF THE FITNESS FUNCTION 11 2.1.3 TYPES OF REPRESENTATIONS
13 2.2 GENETIC AND EVOLUTIONARY ALGORITHMS 15 2.2.1 PRINCIPLES 15 2.2.2
FUNCTIONALITY 16 2.2.3 SCHEMA THEOREM AND BUILDING BLOCK HYPOTHESIS 18
2.3 PROBLEM DIFFICULTY 22 2.3.1 REASONS FOR PROBLEM DIFFICULTY 22 2.3.2
MEASUREMENTS OF PROBLEM DIFFICULTY 25 2.4 EXISTING RECOMMENDATIONS FOR
THE DESIGN OF EFFICIENT REPRESENTATIONS 28 2.4.1 GOLDBERG'S MEANINGFUL
BUILDING BLOCKS AND MINIMAL ALPHABETS 28 2.4.2 RADCLIFFE'S FORMAE AND
EQUIVALENCE CLASSES 29 2.4.3 PALMER'S TREE ENCODING ISSUES 31 2.4.4
RONALD'S REPRESENTATIONAL REDUNDANCY 31 3 THREE ELEMENTS OF A THEORY OF
REPRESENTATIONS 33 3.1 REDUNDANCY 35 3.1.1 REDUNDANT REPRESENTATIONS AND
NEUTRAL NETWORKS 35 3.1.2 SYNONYMOUSLY AND NON-SYNONYMOUSLY REDUNDANT
REPRESENTATIONS 38 3.1.3 COMPLEXITY MODEL FOR REDUNDANT REPRESENTATIONS
45 XIV CONTENTS 3.1.4 POPULATION SIZING FOR SYNONYMOUSLY REDUNDANT
REPRESENTATIONS 47 3.1.5 RUN DURATION AND OVERALL PROBLEM COMPLEXITY FOR
SYNONYMOUSLY REDUNDANT REPRESENTATIONS 49 3.1.6 ANALYZING THE REDUNDANT
TRIVIAL VOTING MAPPING 50 3.1.7 CONCLUSIONS AND FURTHER RESEARCH 57 3.2
SCALING 59 3.2.1 DEFINITIONS AND BACKGROUND 59 3.2.2 POPULATION SIZING
MODEL FOR EXPONENTIALLY SCALED REPRESENTATIONS NEGLECTING THE EFFECT OF
GENETIC DRIFT . 61 3.2.3 POPULATION SIZING MODEL FOR EXPONENTIALLY
SCALED REPRESENTATIONS CONSIDERING THE EFFECT OF GENETIC DRIFT 65 3.2.4
EMPIRICAL RESULTS FOR BINLNT PROBLEMS 68 3.2.5 CONCLUSIONS 72 3.3
LOCALITY 73 3.3.1 INFLUENCE OF REPRESENTATIONS ON PROBLEM DIFFICULTY 74
3.3.2 METRICS, LOCALITY, AND MUTATION OPERATORS 76 3.3.3
PHENOTYPE-FITNESS MAPPINGS AND PROBLEM DIFFICULTY . 78 3.3.4 INFLUENCE
OF LOCALITY ON PROBLEM DIFFICULTY 81 3.3.5 DISTANCE DISTORTION AND
CROSSOVER OPERATORS 84 3.3.6 MODIFYING BB-COMPLEXITY FOR THE ONE-MAX
PROBLEM . . 86 3.3.7 EMPIRICAL RESULTS 89 3.3.8 CONCLUSIONS 93 3.4
SUMMARY AND CONCLUSIONS 95 4 TIME-QUALITY FRAMEWORK FOR A THEORY-BASED
ANALYSIS AND DESIGN OF REPRESENTATIONS 97 4.1 SOLUTION QUALITY AND TIME
TO CONVERGENCE 98 4.2 ELEMENTS OF THE FRAMEWORK 99 4.2.1 REDUNDANCY 99
4.2.2 SCALING 100 4.2.3 LOCALITY 101 4.3 THE FRAMEWORK 102 4.3.1
UNIFORMLY SCALED REPRESENTATIONS 104 4.3.2 EXPONENTIALLY SCALED
REPRESENTATIONS 105 4.4 IMPLICATIONS FOR THE DESIGN OF REPRESENTATIONS
108 4.4.1 UNIFORMLY REDUNDANT REPRESENTATIONS ARE ROBUST . 108 4.4.2
EXPONENTIALLY SCALED REPRESENTATIONS ARE FAST, BUT INACCURATE ILL 4.4.3
LOW-LOCALITY REPRESENTATIONS ARE DIFFICULT TO PREDICT, AND NO GOOD
CHOICE 112 4.5 SUMMARY AND CONCLUSIONS 114 CONTENTS XV ANALYSIS OF
BINARY REPRESENTATIONS OF INTEGERS 117 5.1 INTEGER OPTIMIZATION PROBLEMS
118 5.2 BINARY STRING REPRESENTATIONS 120 5.3 A THEORETICAL COMPARISON
123 5.3.1 REDUNDANCY AND THE UNARY ENCODING 123 5.3.2 SCALING,
MODIFICATION OF PROBLEM DIFFICULTY, AND THE BINARY ENCODING 126 5.3.3
MODIFICATION OF PROBLEM DIFFICULTY AND THE GRAY ENCODING 127 5.4
EXPERIMENTAL RESULTS 129 5.4.1 INTEGER ONE-MAX PROBLEM AND DECEPTIVE
INTEGER ONE-MAX PROBLEM 129 5.4.2 MODIFICATIONS OF THE INTEGER ONE-MAX
PROBLEM 134 5.5 SUMMARY AND CONCLUSIONS 139 ANALYSIS AND DESIGN OF
REPRESENTATIONS FOR TREES 141 6.1 THE TREE DESIGN PROBLEM 142 6.1.1
DEFINITIONS 142 6.1.2 METRICS AND DISTANCES 144 6.1.3 TREE STRUCTURES
145 6.1.4 SCHEMA ANALYSIS FOR GRAPHS 146 6.1.5 SCALABLE TEST PROBLEMS
FOR GRAPHS 147 6.1.6 TREE ENCODING ISSUES 150 6.2 PRIIFER NUMBERS 151
6.2.1 HISTORICAL REVIEW 152 6.2.2 CONSTRUCTION 154 6.2.3 PROPERTIES 156
6.2.4 THE LOW LOCALITY OF THE PRIIFER NUMBER ENCODING 157 6.2.5 SUMMARY
AND CONCLUSIONS 169 6.3 THE CHARACTERISTIC VECTOR ENCODING 171 6.3.1
ENCODING TREES WITH CHARACTERISTIC VECTORS 171 6.3.2 REPAIRING INVALID
SOLUTIONS 172 6.3.3 BIAS AND NON-SYNONYMOUS REDUNDANCY 173 6.3.4 SUMMARY
177 6.4 THE LINK AND NODE BIASED ENCODING 178 6.4.1 MOTIVATION AND
FUNCTIONALITY 179 6.4.2 BIAS AND NON-UNIFORMLY REDUNDANT
REPRESENTATIONS. . . 183 6.4.3 THE NODE-BIASED ENCODING 184 6.4.4 A
CONCEPT FOR THE ANALYSIS OF REDUNDANT REPRESENTATIONS 187 6.4.5
POPULATION SIZING FOR THE LINK-BIASED ENCODING 191 6.4.6 THE
LINK-AND-NODE-BIASED ENCODING 195 6.4.7 EXPERIMENTAL RESULTS 197 6.4.8
CONCLUSIONS 200 6.5 NETWORK RANDOM KEYS (NETKEYS) 201 XVI CONTENTS 6.5.1
MOTIVATION 202 6.5.2 FUNCTIONALITY 202 6.5.3 PROPERTIES 207 6.5.4
UNIFORM REDUNDANCY 208 6.5.5 POPULATION SIZING AND RUN DURATION FOR THE
ONE-MAX TREE PROBLEM 210 6.5.6 CONCLUSIONS 212 6.6 CONCLUSIONS 213 7
ANALYSIS AND DESIGN OF SEARCH OPERATORS FOR TREES 217 7.1 NETDIR: A
DIRECT REPRESENTATION FOR TREES 218 7.1.1 HISTORICAL REVIEW 218 7.1.2
PROPERTIES OF DIRECT REPRESENTATIONS 219 7.1.3 OPERATORS FOR NETDIR 220
7.1.4 SUMMARY 223 7.2 THE EDGE-SET ENCODING 224 7.2.1 FUNCTIONALITY 225
7.2.2 BIAS 227 7.2.3 PERFORMANCE FOR THE OCST PROBLEM 230 7.2.4 SUMMARY
AND CONCLUSIONS 237 8 PERFORMANCE OF GENETIC AND EVOLUTIONARY ALGORITHMS
ON TREE PROBLEMS 241 8.1 GEA PERFORMANCE ON SCALABLE TEST TREE PROBLEMS
242 8.1.1 ANALYSIS OF REPRESENTATIONS 242 8.1.2 ONE-MAX TREE PROBLEM 246
8.1.3 DECEPTIVE TRAP PROBLEM FOR TREES 251 8.2 GEA PERFORMANCE ON THE
OCST PROBLEM 256 8.2.1 THE OPTIMAL COMMUNICATION SPANNING TREE PROBLEM .
. 257 8.2.2 OPTIMIZATION METHODS FOR THE OPTIMAL COMMUNICATION SPANNING
TREE PROBLEM 258 8.2.3 DESCRIPTION OF TEST PROBLEMS 260 8.2.4 ANALYSIS
OF REPRESENTATIONS 262 8.2.5 THEORETICAL PREDICTIONS ON THE PERFORMANCE
OF REPRESENTATIONS 264 8.2.6 EXPERIMENTAL RESULTS 266 8.3 SUMMARY 272 9
SUMMARY AND CONCLUSIONS 275 9.1 SUMMARY 275 9.2 CONCLUSIONS 277 CONTENTS
XVII A OPTIMAL COMMUNICATION SPANNING TREE TEST INSTANCES . 281 A.I
PALMER'S TEST INSTANCES 281 A.2 RAIDL'S TEST INSTANCES 285 A.3 BERRY'S
TEST INSTANCES 289 A.4 REAL WORLD PROBLEMS 291 LIST OF SYMBOLS 315 LIST
OF ACRONYMS 319 INDEX 321 |
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| author | Rothlauf, Franz 1971- |
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| discipline | Informatik Mathematik |
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| edition | 2. ed. |
| format | Book |
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| genre | 1\p (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV021685059 |
| illustrated | Illustrated |
| index_date | 2024-07-02T15:12:39Z |
| indexdate | 2024-07-20T07:52:38Z |
| institution | BVB |
| isbn | 9783540250593 354025059X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014899216 |
| oclc_num | 181443510 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | XVII, 325 S. graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| record_format | marc |
| spelling | Rothlauf, Franz 1971- Verfasser (DE-588)123759978 aut Representations for genetic and evolutionary algorithms Franz Rothlauf 2. ed. Berlin [u.a.] Springer 2006 XVII, 325 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. 295 - 314 Algorithmes génétiques Algoritmos genéticos larpcal Computação evolutiva larpcal Programmation génétique (Informatique) Programmation évolutive Representação de grupos larpcal Représentations d'algèbres Représentations de groupes Evolutionary programming (Computer science) Genetic algorithms Genetic programming (Computer science) Representations of algebras Representations of groups Leistungsbewertung (DE-588)4167271-9 gnd rswk-swf Darstellung Mathematik (DE-588)4128289-9 gnd rswk-swf Binärdarstellung (DE-588)4370795-6 gnd rswk-swf Genetischer Algorithmus (DE-588)4265092-6 gnd rswk-swf Baum Mathematik (DE-588)4004849-4 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Evolutionärer Algorithmus (DE-588)4366912-8 s Binärdarstellung (DE-588)4370795-6 s Baum Mathematik (DE-588)4004849-4 s Darstellung Mathematik (DE-588)4128289-9 s Leistungsbewertung (DE-588)4167271-9 s DE-604 Genetischer Algorithmus (DE-588)4265092-6 s HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014899216&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Rothlauf, Franz 1971- Representations for genetic and evolutionary algorithms Algorithmes génétiques Algoritmos genéticos larpcal Computação evolutiva larpcal Programmation génétique (Informatique) Programmation évolutive Representação de grupos larpcal Représentations d'algèbres Représentations de groupes Evolutionary programming (Computer science) Genetic algorithms Genetic programming (Computer science) Representations of algebras Representations of groups Leistungsbewertung (DE-588)4167271-9 gnd Darstellung Mathematik (DE-588)4128289-9 gnd Binärdarstellung (DE-588)4370795-6 gnd Genetischer Algorithmus (DE-588)4265092-6 gnd Baum Mathematik (DE-588)4004849-4 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| subject_GND | (DE-588)4167271-9 (DE-588)4128289-9 (DE-588)4370795-6 (DE-588)4265092-6 (DE-588)4004849-4 (DE-588)4366912-8 (DE-588)4113937-9 |
| title | Representations for genetic and evolutionary algorithms |
| title_auth | Representations for genetic and evolutionary algorithms |
| title_exact_search | Representations for genetic and evolutionary algorithms |
| title_exact_search_txtP | Representations for genetic and evolutionary algorithms |
| title_full | Representations for genetic and evolutionary algorithms Franz Rothlauf |
| title_fullStr | Representations for genetic and evolutionary algorithms Franz Rothlauf |
| title_full_unstemmed | Representations for genetic and evolutionary algorithms Franz Rothlauf |
| title_short | Representations for genetic and evolutionary algorithms |
| title_sort | representations for genetic and evolutionary algorithms |
| topic | Algorithmes génétiques Algoritmos genéticos larpcal Computação evolutiva larpcal Programmation génétique (Informatique) Programmation évolutive Representação de grupos larpcal Représentations d'algèbres Représentations de groupes Evolutionary programming (Computer science) Genetic algorithms Genetic programming (Computer science) Representations of algebras Representations of groups Leistungsbewertung (DE-588)4167271-9 gnd Darstellung Mathematik (DE-588)4128289-9 gnd Binärdarstellung (DE-588)4370795-6 gnd Genetischer Algorithmus (DE-588)4265092-6 gnd Baum Mathematik (DE-588)4004849-4 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| topic_facet | Algorithmes génétiques Algoritmos genéticos Computação evolutiva Programmation génétique (Informatique) Programmation évolutive Representação de grupos Représentations d'algèbres Représentations de groupes Evolutionary programming (Computer science) Genetic algorithms Genetic programming (Computer science) Representations of algebras Representations of groups Leistungsbewertung Darstellung Mathematik Binärdarstellung Genetischer Algorithmus Baum Mathematik Evolutionärer Algorithmus Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014899216&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT rothlauffranz representationsforgeneticandevolutionaryalgorithms |