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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg
Springer
[2011]
|
| Series: | Studies in computational intelligence
367 |
| Subjects: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=3831164&prov=M&dok_var=1&dok_ext=htm http://deposit.dnb.de/cgi-bin/dokserv?id=4389967&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=024178185&sequence=000001&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=024178185&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024178185&sequence=000005&line_number=0003&func_code=DB_RECORDS&service_type=MEDIA |
| Item Description: | Literaturangaben |
| Physical Description: | XII, 171 Seiten Illustrationen, Diagramme |
| ISBN: | 9783642220838 9783642268687 3642220835 3642268684 |
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| 100 | 1 | |a Luque, Gabriel |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Parallel genetic algorithms |b theory and real world applications |c Gabriel Luque and Enrique Alba |
| 264 | 1 | |a Berlin ; Heidelberg |b Springer |c [2011] | |
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Record in the Search Index
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| adam_text | IMAGE 1
CONTENTS
PART I: INTRODUCTION 1 INTRODUCTION 3
1.1 OPTIMIZATION 4
1.2 METAHEURITICS 5
1.3 EVOLUTIONARY ALGORITHMS 7
1.4 DECENTRALIZED GENETIC ALGORITHMS 10
1.5 CONCLUSIONS 12
2 PARALLEL MODELS FOR GENETIC ALGORITHMS 15
2.1 PANMICTIC GENETIC ALGORITHMS 17
2.2 STRUCTURED GENETIC ALGORITHMS 18
2.3 PARALLEL GENETIC ALGORITHMS 19
2.3.1 PARALLEL MODELS 20
2.3.2 A BRIEF SURVEY ON PARALLEL GAS 23
2.3.3 NEW TRENDS IN PGAS 25
2.4 FIRST EXPERIMENTAL RESULTS 26
2.4.1 MAXSAT PROBLEM 26
2.4.2 ANALYSIS OF RESULTS 27
2.5 SUMMARY 29
3 BEST PRACTICES IN REPORTING RESULTS WITH PARALLEL GENETIC ALGORITHMS
31
3.1 PARALLEL PERFORMANCE MEASURES 32
3.1.1 SPEEDUP 32
3.1.2 OTHER PARALLEL MEASURES 36
3.2 HOW TO REPORT RESULTS IN PGAS 37
3.2.1 EXPERIMENTATION 37
3.2.2 MEASURING PERFORMANCE 39
3.2.3 QUALITY OF THE SOLUTIONS 39
3.2.4 COMPUTATIONAL EFFORT 40
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1012138763
DIGITALISIERT DURCH
IMAGE 2
X CONTENTS
3.2.5 STATISTICAL ANALYSIS 41
3.2.6 REPORTING RESULTS 42
3.3 INADEQUATE UTILIZATION OF PARALLEL METRICS 43
3.4 ILLUSTRATING THE INFLUENCE OF MEASURES 45
3.4.1 EXAMPLE 1: ON THE ABSENCE OF INFORMATION 46
3.4.2 EXAMPLE 2: RELAXING THE OPTIMUM HELPS 47
3.4.3 EXAMPLE 3: CLEAR CONCLUSIONS DO EXIST 48
3.4.4 EXAMPLE 4: MEANINGFULNESS DOES NOT MEAN CLEAR SUPERIORITY 48
3.4.5 EXAMPLE 5: SPEEDUP: AVOID COMPARING APPLES AGAINST ORANGES 49
3.4.6 EXAMPLE 6: A PREDEFINED EFFORT COULD HINDER CLEAR CONCLUSIONS 50
3.5 CONCLUSIONS 51
PART II: CHARACTERIZATION OF PARALLEL GENETIC ALGORITHMS
4 THEORETICAL MODELS OF SELECTION PRESSURE FOR DISTRIBUTED GAS 55
4.1 EXISTING THEORETICAL MODELS 57
4.1.1 THE LOGISTIC MODEL 57
4.1.2 THE HYPERGRAPH MODEL 57
4.1.3 OTHER MODELS 58
4.2 ANALYZED MODELS 59
4.3 EFFECTS OF THE MIGRATION POLICY ON THE ACTUAL GROWTH CURVES 60
4.3.1 PARAMETERS 61
4.3.2 MIGRATION TOPOLOGY 61
4.3.3 MIGRATION FREQUENCY 63
4.3.4 MIGRATION RATE 64
4.3.5 ANALYSIS OF THE RESULTS 65
4.4 TAKEOVER TIME ANALYSIS 68
4.5 CONCLUSIONS 71
PART III: APPLICATIONS OF PARALLEL GENETIC ALGORITHMS
5 NATURAL LANGUAGE TAGGING WITH PARALLEL GENETIC ALGORITHMS 75
5.1 STATISTICAL TAGGING 77
5.2 AUTOMATIC TAGGING WITH METAHEURISTICS 79
5.2.1 GENETIC ALGORITHM 79
5.2.2 CHC ALGORITHM 80
5.2.3 SIMULATED ANNEALING 80
5.2.4 PARALLEL VERSIONS 80
IMAGE 3
CONTENTS XI
5.3 ALGORITHM DECISIONS: REPRESENTATION, EVALUATION, AND OPERATORS 81
5.3.1 INDIVIDUALS 81
5.3.2 FITNESS EVALUATION 82
5.3.3 GENETIC OPERATORS 82
5.4 EXPERIMENTAL DESIGN AND ANALYSIS 83
5.5 CONCLUSIONS 89
DESIGN OF COMBINATIONAL LOGIC CIRCUITS 91
6.1 PROBLEM DEFINITION 92
6.2 ENCODING SOLUTIONS INTO STRINGS 94
6.3 RELATED WORKS 97
6.4 SEQUENTIAL, PARALLEL, AND HYBRID APPROACHES 97
6.5 COMPUTATIONAL EXPERIMENTS AND ANALYSIS OF THEIR RESULTS 102
6.5.1 CASE STUDY 1: SASAO 104
6.5.2 CASE STUDY 2: CATHERINE 106
6.5.3 CASE STUDY 3: KATZ 1 108
6.5.4 CASE STUDY 4: 2-BIT MULTIPLIER 109
6.5.5 CASE STUDY 5: KATZ 2 110
6.6 OVERALL DISCUSSION 113
6.7 CONCLUSIONS AND FUTURE WORK 114
PARALLEL GENETIC ALGORITHM FOR THE WORKFORCE PLANNING PROBLEM 115
7.1 THE WORKFORCE PLANNING PROBLEM 116
7.2 DESIGN OF A GENETIC ALGORITHM 118
7.2.1 SOLUTION ENCODING 118
7.2.2 EVALUATION THE QUALITY OF A SOLUTION 119
7.2.3 REPAIRING/IMPROVING OPERATOR 120
7.2.4 RECOMBINATION OPERATOR 120
7.2.5 MUTATION OPERATOR 122
7.2.6 THE PROPOSED PARALLEL GA 122
7.3 SCATTER SEARCH 123
7.3.1 SEEDING THE INITIAL POPULATION 124
7.3.2 IMPROVEMENT METHOD 124
7.3.3 PARALLEL SS 125
7.4 COMPUTATIONAL EXPERIMENTS AND ANALYSIS OF RESULTS 125 7.4.1 PROBLEM
INSTANCES 126
7.4.2 RESULTS: WORKFORCE PLANNING PERFORMANCE 126 7.4.3 RESULTS:
COMPUTATIONAL TIMES 129
7.4.4 A PARALLEL HYBRID GA 132
7.5 CONCLUSIONS 134
IMAGE 4
XII CONTENTS
8 PARALLEL GAS IN BIOINFORMATICS: ASSEMBLING D NA FRAGMENTS 135
8.1 THE WORK OF A DNA FRAGMENT ASSEMBLER 136
8.1.1 DNA SEQUENCING PROCESS 136
8.2 RELATED LITERATURE 139
8.3 THE PGA DNA ASSEMBLER 140
8.3.1 SOLUTION ENCODING 140
8.3.2 SOLUTION EVALUATION 140
8.3.3 GENETIC OPERATORS 141
8.3.4 THE PARALLEL APPROACH 142
8.4 EXPERIMENTAL VALIDATION 143
8.4.1 TARGET PROBLEM INSTANCES 143
8.4.2 PARAMETERIZATION 144
8.4.3 ANALYSIS OF RESULTS 144
8.5 CONCLUSIONS 147
A THE MALLBA LIBRARY 149
A.I SKELETON INTERFACES 151
A.2 COMMUNICATION INTERFACE 152
A.3 HYBRIDIZATION INTERFACE 154
A.4 ADDITIONAL INFORMATION ABOUT MALLBA 156
B ACRONYMS 157
REFERENCES 159
Contents
Part I: Introduction
1 Introduction................................................ 3
1.1 Optimization.............................................. 4
1.2 Metaheuritics............................................. 5
1.3 Evolutionary Algorithms.................................. 7
1.4 Decentralized Genetic Algorithms......................... 10
1.5 Conclusions.............................................. 12
2 Parallel Models for Genetic Algorithms...................... 15
2.1 Panmictic Genetic Algorithms ............................ 17
2.2 Structured Genetic Algorithms............................ 18
2.3 Parallel Genetic Algorithms. . .......................... 19
2.3.1 Parallel Models.................................. 20
2.3.2 A Brief Survey on Parallel GAs..................... 23
2.3.3 New Trends in pGAs ................................ 25
2.4 First Experimental Results............................... 26
2.4.1 MAXSAT Problem..................................... 26
2.4.2 Analysis of Results................................ 27
2.5 Summary.................................................. 29
3 Best Practices in Reporting Results with Parallel
Genetic Algorithms.......................................... 31
3.1 Parallel Performance Measures............................ 32
3.1.1 Speedup ........................................... 32
3.1.2 Other Parallel Measures............................ 36
3.2 How to Report Results in pGAs........................ 37
3.2.1 Experimentation.................................... 37
3.2.2 Measuring Performance.............................. 39
3.2.3 Quality of the Solutions.......................... 39
3.2.4 Computational Effort............................... 40
X Contents
3.2.5 Statistical Analysis ................................. 41
3.2.6 Reporting Results .................................. 42
3.3 Inadequate Utilization of Parallel Metrics.................. 43
3.4 Illustrating the Influence of Measures...................... 45
3.4.1 Example 1: On the Absence of Information.............. 46
3.4.2 Example 2: Relaxing the Optimum Helps................. 47
3.4.3 Example 3: Clear Conclusions do Exist................. 48
3.4.4 Example 4: Meaningfulness Does Not Mean Clear
Superiority........................................... 48
3.4.5 Example 5: Speedup: Avoid Comparing Apples
against Oranges....................................... 49
3.4.6 Example 6: A Predefined Effort Could Hinder Clear
Conclusions........................................... 50
3.5 Conclusions................................................. 51
Part II: Characterization of Parallel Genetic Algorithms
4 Theoretical Models of Selection Pressure for
Distributed GAs............................................. 55
4.1 Existing Theoretical Models.............................. 57
4.1.1 The Logistic Model................................. 57
4.1.2 The Hypergraph Model............................... 57
4.1.3 Other Models....................................... 58
4.2 Analyzed Models........................................ 59
4.3 Effects of the Migration Policy on the Actual Growth
Curves................................................. 60
4.3.1 Parameters . .................................... 61
4.3.2 Migration Topology................................. 61
4.3.3 Migration Frequency................................ 63
4.3.4 Migration Rate..................................... 64
4.3.5 Analysis of the Results............................ 65
4.4 Takeover Time Analysis................................... 68
4.5 Conclusions............................................ 71
Part III: Applications of Parallel Genetic Algorithms
5 Natural Language Tagging with Parallel
Genetic Algorithms............................................. 75
5.1 Statistical Tagging................................... 77
5.2 Automatic Tagging with Metaheuristics.................... 79
5.2.1 Genetic Algorithm................................... 79
5.2.2 CHC Algorithm....................................... 80
5.2.3 Simulated Annealing................................. 80
5.2.4 Parallel Versions................................ 80
Contents
XI
5.3 Algorithm Decisions: Representation, Evaluation, and
Operators................................................. 81
5.3.1 Individuals......................................... 81
5.3.2 Fitness Evaluation.................................. 82
5.3.3 Genetic Operators .................................. 82
5.4 Experimental Design and Analysis.......................... 83
5.5 Conclusions............................................... 89
6 Design of Combinational Logic Circuits....................... 91
6.1 Problem Definition ....................................... 92
6.2 Encoding Solutions into Strings........................... 94
6.3 Related Works............................................. 97
6.4 Sequential, Parallel, and Hybrid Approaches............... 97
6.5 Computational Experiments and Analysis of Their
Results.................................................. 102
6.5.1 Case Study 1: Sasao................................ 104
6.5.2 Case Study 2: Catherine.......................... 106
6.5.3 Case Study 3: Katz 1............................... 108
6.5.4 Case Study 4: 2-Bit Multiplier................... 109
6.5.5 Case Study 5: Katz 2.............................. 110
6.6 Overall Discussion....................................... 113
6.7 Conclusions and Future Work.............................. 114
7 Parallel Genetic Algorithm for the Workforce Planning
Problem...................................................... 115
7.1 The Workforce Planning Problem .......................... 116
7.2 Design of a Genetic Algorithm............................ 118
7.2.1 Solution Encoding.................................. 118
7.2.2 Evaluation the Quality of a Solution............... 119
7.2.3 Repairing/Improving Operator....................... 120
7.2.4 Recombination Operator............................. 120
7.2.5 Mutation Operator.................................. 122
7.2.6 The Proposed Parallel GA........................... 122
7.3 Scatter Search........................................... 123
7.3.1 Seeding the Initial Population..................... 124
7.3.2 Improvement Method................................. 124
7.3.3 Parallel SS........................................ 125
7.4 Computational Experiments and Analysis of Results........ 125
7.4.1 Problem Instances................................. 126
7.4.2 Results: Workforce Planning Performance............ 126
7.4.3 Results: Computational Times....................... 129
7.4.4 A Parallel Hybrid GA............................... 132
7.5 Conclusions.............................................. 134
XII Contents
8 Parallel GAs in Bioinformatics: Assembling DNA
Fragments................................................... 135
8.1 The Work of a DNA Fragment Assembler.................... 136
8.1.1 DNA Sequencing Process............................ 136
8.2 Related Literature...................................... 139
8.3 The pGA DNA Assembler................................... 140
8.3.1 Solution Encoding................................. 140
8.3.2 Solution Evaluation............................... 140
8.3.3 Genetic Operators................................. 141
8.3.4 The Parallel Approach............................. 142
8.4 Experimental Validation................................. 143
8.4.1 Target Problem Instances.......................... 143
8.4.2 Parameterization.................................. 144
8.4.3 Analysis of Results............................... 144
8.5 Conclusions............................................. 147
A The MALLBA Library ............................................ 149
A.l Skeleton Interfaces................................... 151
A. 2 Communication Interface................................. 152
A.3 Hybridization Interface................................. 154
A.4 Additional Information about MALLBA..................... 156
B Acronyms....................................................... 157
References....................................................... 159
Contents
Part I: Introduction 1 2 3
1 Introduction............................................... 3
1.1 Optimization.............................................. 4
1.2 Metaheuritics............................................. 5
1.3 Evolutionary Algorithms.................................. 7
1.4 Decentralized Genetic Algorithms......................... 10
1.5 Conclusions............................................ 12
2 Parallel Models for Genetic Algorithms...................... 15
2.1 Panmictic Genetic Algorithms ............................ 17
2.2 Structured Genetic Algorithms............................ 18
2.3 Parallel Genetic Algorithms.............................. 19
2.3.1 Parallel Models.................................. 20
2.3.2 A Brief Survey on Parallel GAs..................... 23
2.3.3 New Trends in pGAs .............................. 25
2.4 First Experimental Results............................. 26
2.4.1 MAXSAT Problem..................................... 26
2.4.2 Analysis of Results................................ 27
2.5 Summary................................................ 29
3 Best Practices in Reporting Results with Parallel
Genetic Algorithms.......................................... 31
3.1 Parallel Performance Measures............................ 32
3.1.1 Speedup .......................................... 32
3.1.2 Other Parallel Measures ......................... 36
3.2 How to Report Results in pGAs............................ 37
3.2.1 Experimentation.................................... 37
3.2.2 Measuring Performance.............................. 39
3.2.3 Quality of the Solutions........................... 39
3.2.4 Computational Effort............................... 40
X
Contents
3.2.5 Statistical Analysis.................................. 41
3.2.6 Reporting Results .................................... 42
3.3 Inadequate Utilization of Parallel Metrics.................. 43
3.4 Illustrating the Influence of Measures.................... 45
3.4.1 Example 1: On the Absence of Information.............. 46
3.4.2 Example 2; Relaxing the Optimum Helps................. 47
3.4.3 Example 3: Clear Conclusions do Exist................. 48
3.4.4 Example 4: Meaningfulness Does Not Mean Clear
Superiority........................................... 48
3.4.5 Example 5: Speedup: Avoid Comparing Apples
against Oranges....................................... 49
3.4.6 Example 6: A Predefined Effort Could Hinder Clear
Conclusions......................................... 50
3.5 Conclusions................................................. 51
Part II: Characterization of Parallel Genetic Algorithms
4 Theoretical Models of Selection Pressure for
Distributed GAs............................................. 55
4.1 Existing Theoretical Models............................. 57
4.1.1 The Logistic Model................................. 57
4.1.2 The Hypergraph Model............................... 57
4.1.3 Other Models...................................... 58
4.2 Analyzed Models.......................................... 59
4.3 Effects of the Migration Policy on the Actual Growth
Curves................................................... 60
4.3.1 Parameters ........................................ 61
4.3.2 Migration Topology................................. 61
4.3.3 Migration Frequency ............................... 63
4.3.4 Migration Rate .................................. 64
4.3.5 Analysis of the Results ........................... 65
4.4 Takeover Time Analysis................................... 68
4.5 Conclusions.............................................. 71
Part III: Applications of Parallel Genetic Algorithms
5 Natural Language Tagging with Parallel
Genetic Algorithms............................................. 75
5.1 Statistical Tagging..................................... 77
5.2 Automatic Tagging with Metaheuristics................... 79
5.2.1 Genetic Algorithm................................. 79
5.2.2 CHC Algorithm....................................... 80
5.2.3 Simulated Annealing................................. 80
5.2.4 Parallel Versions.................................. 80
Contents
XI
5 3 Algorithm Decisions: Representation, Evaluation, and
Operators................................................. 81
5.3.1 Individuals......................................... 81
5.3.2 Fitness Evaluation.................................. 82
5.3.3 Genetic Operators................................. 82
5.4 Experimental Design and Analysis.......................... 83
5.5 Conclusions............................................... 89
6 Design of Combinational Logic Circuits....................... 91
6.1 Problem Definition....................................... 92
6.2 Encoding Solutions into Strings........................... 94
6.3 Related Works............................................. 97
6.4 Sequential, Parallel, and Hybrid Approaches............... 97
6.5 Computational Experiments and Analysis of Their
Results.................................................. 102
6.5.1 Case Study 1: Sasao................................ 104
6.5.2 Case Study 2: Catherine.......................... 106
6.5.3 Case Study 3: Katz 1............................... 108
6.5.4 Case Study 4: 2-Bit Multiplier .................... 109
6.5.5 Case Study 5: Katz 2............................... 110
6.6 Overall Discussion....................................... 113
6.7 Conclusions and Future Work.............................. 114
7 Parallel Genetic Algorithm for the Workforce Planning
Problem...................................................... 115
7.1 The Workforce Planning Problem .......................... 116
7.2 Design of a Genetic Algorithm............................ 118
7.2.1 Solution Encoding.................................. 118
7.2.2 Evaluation the Quality of a Solution . ............ 119
7.2.3 Repairing/Improving Operator....................... 120
7.2.4 Recombination Operator............................. 120
7.2.5 Mutation Operator.................................. 122
7.2.6 The Proposed Parallel GA........................... 122
7.3 Scatter Search........................................... 123
7.3.1 Seeding the Initial Population..................... 124
7.3.2 Improvement Method................................. 124
7.3.3 Parallel SS ....................................... 125
7.4 Computational Experiments and Analysis of Results........ 125
7.4.1 Problem Instances............................... 126
7.4.2 Results: Workforce Planning Performance............ 126
7.4.3 Results: Computational Times....................... 129
7.4.4 A Parallel Hybrid GA............................... 132
7.5 Conclusions.............................................. 134
XII Contents
8 Parallel G As in Bioinfor matics: Assembling DNA
Fragments.................................................. 135
8.1 The Work of a DNA Fragment Assembler.................... 136
8.1.1 DNA Sequencing Process............................ 136
8.2 Related Literature...................................... 139
8.3 The pGA DNA Assembler................................... 140
8.3.1 Solution Encoding................................. 140
8.3.2 Solution Evaluation............................... 140
8.3.3 Genetic Operators................................. 141
8.3.4 The Parallel Approach............................. 142
8.4 Experimental Validation................................. 143
8.4.1 Target Problem Instances.......................... 143
8.4.2 Parameterization.................................. 144
8.4.3 Analysis of Results............................... 144
8.5 Conclusions........................................... 147
A The MALLBA Library .......................................... 149
A.l Skeleton Interfaces..................................... 151
A. 2 Communication Interface................................. 152
A.3 Hybridization Interface................................. 154
A.4 Additional Information about MALLBA..................... 156
B Acronyms..................................................... 157
References....................................................... 159
|
| any_adam_object | 1 |
| author | Luque, Gabriel Alba, Enrique |
| author_facet | Luque, Gabriel Alba, Enrique |
| author_role | aut aut |
| author_sort | Luque, Gabriel |
| author_variant | g l gl e a ea |
| building | Verbundindex |
| bvnumber | BV039160680 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)746282544 (DE-599)DNB1012138763 |
| dewey-full | 519.62 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.62 |
| dewey-search | 519.62 |
| dewey-sort | 3519.62 |
| dewey-tens | 510 - Mathematics |
| discipline | Maschinenbau / Maschinenwesen Informatik Mathematik |
| format | Book |
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| id | DE-604.BV039160680 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T15:51:27Z |
| institution | BVB |
| isbn | 9783642220838 9783642268687 3642220835 3642268684 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024178185 |
| oclc_num | 746282544 |
| open_access_boolean | |
| owner | DE-11 DE-739 |
| owner_facet | DE-11 DE-739 |
| physical | XII, 171 Seiten Illustrationen, Diagramme |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spellingShingle | Luque, Gabriel Alba, Enrique Parallel genetic algorithms theory and real world applications Studies in computational intelligence Genetischer Algorithmus (DE-588)4265092-6 gnd Paralleler Algorithmus (DE-588)4193615-2 gnd |
| subject_GND | (DE-588)4265092-6 (DE-588)4193615-2 |
| title | Parallel genetic algorithms theory and real world applications |
| title_auth | Parallel genetic algorithms theory and real world applications |
| title_exact_search | Parallel genetic algorithms theory and real world applications |
| title_full | Parallel genetic algorithms theory and real world applications Gabriel Luque and Enrique Alba |
| title_fullStr | Parallel genetic algorithms theory and real world applications Gabriel Luque and Enrique Alba |
| title_full_unstemmed | Parallel genetic algorithms theory and real world applications Gabriel Luque and Enrique Alba |
| title_short | Parallel genetic algorithms |
| title_sort | parallel genetic algorithms theory and real world applications |
| title_sub | theory and real world applications |
| topic | Genetischer Algorithmus (DE-588)4265092-6 gnd Paralleler Algorithmus (DE-588)4193615-2 gnd |
| topic_facet | Genetischer Algorithmus Paralleler Algorithmus |
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| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT luquegabriel parallelgeneticalgorithmstheoryandrealworldapplications AT albaenrique parallelgeneticalgorithmstheoryandrealworldapplications |