Cellular genetic algorithms:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer
2008
|
| Schriftenreihe: | Operations research - computer science interfaces series
42 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 245 S. Ill., graph. Darst. |
| ISBN: | 9780387776095 9780387776101 |
Internformat
MARC
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| 084 | |a ST 301 |0 (DE-625)143651: |2 rvk | ||
| 084 | |a 004 |2 sdnb | ||
| 100 | 1 | |a Alba, Enrique |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Cellular genetic algorithms |c Enrique Alba and Bernabé Dorronsoro |
| 264 | 1 | |a New York, NY |b Springer |c 2008 | |
| 300 | |a XIII, 245 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Operations research - computer science interfaces series |v 42 | |
| 650 | 0 | 7 | |a Genetischer Algorithmus |0 (DE-588)4265092-6 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Genetischer Algorithmus |0 (DE-588)4265092-6 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Dorronsoro, Bernabé |e Verfasser |4 aut | |
| 830 | 0 | |a Operations research - computer science interfaces series |v 42 |w (DE-604)BV012124389 |9 42 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016748367&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016748367 | ||
Datensatz im Suchindex
| _version_ | 1804138031893970944 |
|---|---|
| adam_text | Contents
Part I Introduction
1
Introduction to Cellular Genetic Algorithms
............... 3
1.1
Optimization and Advanced Algorithms
.................... 4
1.2
Solving Problems Using Metaheuristics
..................... 6
1.3
Evolutionary Algorithms
................................. 7
1.4
Decentralized Evolutionary Algorithms
..................... 11
1.5
Cellular Evolutionary Algorithms
.......................... 13
1.5.1
Synchronous and Asynchronous cEAs
................ 16
1.5.2
Formal Characterization of the Population in cEAs
.... 17
1.6
Cellular Genetic Algorithms
.............................. 18
1.7
Conclusions
............................................. 20
2
The State of the Art in Cellular Evolutionary Algorithms
. 21
2.1
Cellular EAs: a New Algorithmic Model
.................... 21
2.2
The Research in the Theory of the Cellular Models
.......... 22
2.2.1
Characterizing the Behavior of cEAs
................. 24
2.2.2
The Influence of the Ratio
.......................... 26
2.3
Empirical Studies on the Behavior of cEAs
................. 26
2.4
Algorithmic Improvements to the Canonical Model
.......... 29
2.5
Parallel Models of cEAs
.................................. 31
2.6
Conclusions
............................................. 34
Part II Characterizing Cellular Genetic Algorithms
3
On the Effects of Structuring the Population
.............. 37
3.1
Non-decentralized GAs
................................... 37
3.1.1
Steady State GA
.................................. 38
3.1.2
Generational GA
.................................. 38
3.2
Decentralized GAs
....................................... 39
X
Contents
3.3
Experimental
Comparison
................................ 40
3.3.1
Cellular versus Panmictic GAs
...................... 41
3.3.2
Cellular versus Distributed GAs
..................... 43
3.4
Conclusions
............................................. 46
4
Some Theory: A Selection Pressure Study on cGAs
........ 47
4.1
The Selection Pressure
................................... 48
4.2
Theoretical Study
....................................... 50
4.2.1
Approach to the Deterministic Model
................ 50
4.2.2
A Probabilistic Model for Approaching the Selection
Pressure Curve
.................................... 52
4.2.3
Comparison of the Main Existing Mathematical Models
57
4.3
Validation of the Theoretical Models
....................... 60
4.3.1
Validation on Combinatorial Optimization
............ 61
4.3.2
Validation on Continuous Optimization
.............. 65
4.4
Conclusions
............................................. 68
Part III Algorithmic Models and Extensions
5
Algorithmic and Experimental Design
..................... 73
5.1
Proposal of New Efficient Models
.......................... 73
5.2
Evaluation of the Results
................................. 76
5.2.1
The Mono-objective Case
........................... 77
5.2.2
The Multi-objective Case
........................... 78
5.2.3
Some Additional Definitions
........................ 80
5.3
Conclusions
............................................. 82
6
Design of Self-adaptive cGAs
.............................. 83
6.1
Introduction
............................................ 83
6.2
Description of Algorithms
................................ 84
6.2.1
Static and Pre-Programmed Algorithms
.............. 86
6.2.2
Self-Adaptive Algorithms
........................... 87
6.3
Experimentation
........................................ 90
6.3.1
Parameterization
.................................. 91
6.3.2
Experimental Results
.............................. 92
6.3.3
Additional Discussion
.............................. 95
6.4
Conclusions
............................................. 99
7
Design of Cellular Memetic Algorithms
....................101
7.1
Cellular Memetic Algorithms
..............................102
7.2
Simple and Advanced Components in Cellular
M
As
..........103
7.2.1
Three Basic Local Search Techniques for SAT
.........103
7.2.2
Cellular Memetic GAs
.............................106
7.3
Computational Analysis
..................................107
Contents
XI
7.3.1
Effects of Combining a Structured Population and an
Adaptive Fitness Function (SAW)
...................107
7.3.2
Results:
Non Memetic
Procedures for SAT
............109
7.3.3
Results: Cellular Memetic Algorithms
................110
7.3.4
Comparison Versus Other Algorithms in the Literature.
113
7.4
Conclusions
.............................................114
8
Design of Parallel Cellular Genetic Algorithms
............115
8.1
The Meta-cellular Genetic Algorithm
......................116
8.1.1
Parameterization
..................................117
8.1.2
Analysis of Results
................................117
8.2
The Distributed Cellular Genetic Algorithm
................119
8.2.1
Parameterization
..................................120
8.2.2
Analysis of Results
................................123
8.3
Conclusions
.............................................125
9
Designing Cellular Genetic Algorithms for Multi-objective
Optimization
..............................................127
9.1
Background on Multi-objective Optimization
................129
9.2
The MOCell Algorithm
..................................130
9.2.1
Extensions to MOCell
..............................132
9.3
Experimental Analysis
...................................133
9.4
Conclusions
.............................................138
10
Other Cellular Models
.....................................139
10.1
Hierarchical cGAs
.......................................139
10.1.1
Hierarchy
........................................140
10.1.2
Dissimilarity Selection
.............................141
10.1.3
First Theoretical Results: Takeover Times
............142
10.1.4
Computational Experiments
........................143
10.2
Cellular Estimation of Distribution Algorithms
..............146
10.2.1
First Theoretical Results: Takeover Times
............149
10.2.2
Computational Experiments
........................149
10.3
Conclusions
.............................................152
11
Software for cGAs: The JCell Framework
..................153
11.1
The JCell Framework
....................................153
11.2
Using JCell
.............................................158
11.3
Conclusions
.............................................163
XII Contents
Part IV Applications of cGAs
12
Continuous Optimization
..................................167
12.1
Introduction
............................................167
12.2
Experimentation
........................................168
12.2.1
Tuning the Algorithm
..............................169
12.2.2
Comparison with Other Algorithms
..................171
12.3
Conclusions
.............................................174
13
Logistics: The Vehicle Routing Problem
...................175
13.1
The Vehicle Routing Problem
.............................177
13.2
Proposed Algorithms
....................................178
13.2.1
Problem Representation
............................179
13.2.2
Recombination
....................................180
13.2.3
Mutation
.........................................181
13.2.4
Local Search
......................................182
13.3
Solving CVRP with JCell2oli
.............................184
13.4
New Solutions to CVRP
..................................185
13.5
Conclusions
.............................................186
14
Telecommunications: Optimization of the Broadcasting
Process in MANETs
.......................................187
14.1
The Problem
............................................188
14.1.1
Metropolitan Mobile Ad Hoc Networks. The Madhoc
Simulator
........................................188
14.1.2
Delayed Flooding with Cumulative Neighborhood
.....191
14.1.3
MOPs Definition
..................................192
14.2
A Multi-objective cGA: cMOGA
..........................193
14.2.1
Dealing with Constraints
...........................194
14.3
Experiments
............................................194
14.3.1
Parameterization of cMOGA
........................195
14.3.2
Madhoc Configuration
.............................196
14.3.3
Results for DFCNT
................................198
14.4
Comparing cMOGA Against NSGA-II
.....................200
14.4.1
Parameterization of NSGA-II
.......................200
14.4.2
Discussion
........................................201
14.5
Conclusions
.............................................202
15
Bioinformatics: The
DNA
Fragment Assembly Problem
. .. 203
15.1
The
DNA
Fragment Assembly Problem
....................204
15.2
A cMA for
DNA
Fragment Assembly Problem
..............206
15.3
Results
.................................................208
15.4
Conclusions
.............................................210
Contents XIII
Part V Appendix
A Definition of the Benchmark Problems
.....................213
A.I Combinatorial Optimization Problems
.....................213
A.I.I COUNTSAT Problem
..............................213
A.I.
2
Error Correcting Codes Design Problem
-
ECC
.......214
A.1.3 Frequency Modulation Sounds
-
FMS
................215
A.1.4 IsoPeak Problem
..................................215
A.I.
5
Maximum Cut of a Graph
-
MAXCUT
..............216
A.I.
6
Massively
Multimodal
Deceptive Problem
-
MMDP
... 216
A.1.7 Minimum Tardy Task Problem
-
MTTP
.............217
A.1.8 OneMax Problem
.................................218
A.1.9 Plateau Problem
..................................218
A.I.
10
P-PEAKS Problem
................................218
A.I.
11
Satisfiability Problem
-
SAT
........................219
A.
2
Continuous Optimization Problems
........................220
A.2.1 Academic Problems
................................220
A.2.2 Real World Problems
..............................222
A.3 Multi-objective Optimization Problems
.....................223
References
.....................................................225
Index
..........................................................243
|
| adam_txt |
Contents
Part I Introduction
1
Introduction to Cellular Genetic Algorithms
. 3
1.1
Optimization and Advanced Algorithms
. 4
1.2
Solving Problems Using Metaheuristics
. 6
1.3
Evolutionary Algorithms
. 7
1.4
Decentralized Evolutionary Algorithms
. 11
1.5
Cellular Evolutionary Algorithms
. 13
1.5.1
Synchronous and Asynchronous cEAs
. 16
1.5.2
Formal Characterization of the Population in cEAs
. 17
1.6
Cellular Genetic Algorithms
. 18
1.7
Conclusions
. 20
2
The State of the Art in Cellular Evolutionary Algorithms
. 21
2.1
Cellular EAs: a New Algorithmic Model
. 21
2.2
The Research in the Theory of the Cellular Models
. 22
2.2.1
Characterizing the Behavior of cEAs
. 24
2.2.2
The Influence of the Ratio
. 26
2.3
Empirical Studies on the Behavior of cEAs
. 26
2.4
Algorithmic Improvements to the Canonical Model
. 29
2.5
Parallel Models of cEAs
. 31
2.6
Conclusions
. 34
Part II Characterizing Cellular Genetic Algorithms
3
On the Effects of Structuring the Population
. 37
3.1
Non-decentralized GAs
. 37
3.1.1
Steady State GA
. 38
3.1.2
Generational GA
. 38
3.2
Decentralized GAs
. 39
X
Contents
3.3
Experimental
Comparison
. 40
3.3.1
Cellular versus Panmictic GAs
. 41
3.3.2
Cellular versus Distributed GAs
. 43
3.4
Conclusions
. 46
4
Some Theory: A Selection Pressure Study on cGAs
. 47
4.1
The Selection Pressure
. 48
4.2
Theoretical Study
. 50
4.2.1
Approach to the Deterministic Model
. 50
4.2.2
A Probabilistic Model for Approaching the Selection
Pressure Curve
. 52
4.2.3
Comparison of the Main Existing Mathematical Models
57
4.3
Validation of the Theoretical Models
. 60
4.3.1
Validation on Combinatorial Optimization
. 61
4.3.2
Validation on Continuous Optimization
. 65
4.4
Conclusions
. 68
Part III Algorithmic Models and Extensions
5
Algorithmic and Experimental Design
. 73
5.1
Proposal of New Efficient Models
. 73
5.2
Evaluation of the Results
. 76
5.2.1
The Mono-objective Case
. 77
5.2.2
The Multi-objective Case
. 78
5.2.3
Some Additional Definitions
. 80
5.3
Conclusions
. 82
6
Design of Self-adaptive cGAs
. 83
6.1
Introduction
. 83
6.2
Description of Algorithms
. 84
6.2.1
Static and Pre-Programmed Algorithms
. 86
6.2.2
Self-Adaptive Algorithms
. 87
6.3
Experimentation
. 90
6.3.1
Parameterization
. 91
6.3.2
Experimental Results
. 92
6.3.3
Additional Discussion
. 95
6.4
Conclusions
. 99
7
Design of Cellular Memetic Algorithms
.101
7.1
Cellular Memetic Algorithms
.102
7.2
Simple and Advanced Components in Cellular
M
As
.103
7.2.1
Three Basic Local Search Techniques for SAT
.103
7.2.2
Cellular Memetic GAs
.106
7.3
Computational Analysis
.107
Contents
XI
7.3.1
Effects of Combining a Structured Population and an
Adaptive Fitness Function (SAW)
.107
7.3.2
Results:
Non Memetic
Procedures for SAT
.109
7.3.3
Results: Cellular Memetic Algorithms
.110
7.3.4
Comparison Versus Other Algorithms in the Literature.
113
7.4
Conclusions
.114
8
Design of Parallel Cellular Genetic Algorithms
.115
8.1
The Meta-cellular Genetic Algorithm
.116
8.1.1
Parameterization
.117
8.1.2
Analysis of Results
.117
8.2
The Distributed Cellular Genetic Algorithm
.119
8.2.1
Parameterization
.120
8.2.2
Analysis of Results
.123
8.3
Conclusions
.125
9
Designing Cellular Genetic Algorithms for Multi-objective
Optimization
.127
9.1
Background on Multi-objective Optimization
.129
9.2
The MOCell Algorithm
.130
9.2.1
Extensions to MOCell
.132
9.3
Experimental Analysis
.133
9.4
Conclusions
.138
10
Other Cellular Models
.139
10.1
Hierarchical cGAs
.139
10.1.1
Hierarchy
.140
10.1.2
Dissimilarity Selection
.141
10.1.3
First Theoretical Results: Takeover Times
.142
10.1.4
Computational Experiments
.143
10.2
Cellular Estimation of Distribution Algorithms
.146
10.2.1
First Theoretical Results: Takeover Times
.149
10.2.2
Computational Experiments
.149
10.3
Conclusions
.152
11
Software for cGAs: The JCell Framework
.153
11.1
The JCell Framework
.153
11.2
Using JCell
.158
11.3
Conclusions
.163
XII Contents
Part IV Applications of cGAs
12
Continuous Optimization
.167
12.1
Introduction
.167
12.2
Experimentation
.168
12.2.1
Tuning the Algorithm
.169
12.2.2
Comparison with Other Algorithms
.171
12.3
Conclusions
.174
13
Logistics: The Vehicle Routing Problem
.175
13.1
The Vehicle Routing Problem
.177
13.2
Proposed Algorithms
.178
13.2.1
Problem Representation
.179
13.2.2
Recombination
.180
13.2.3
Mutation
.181
13.2.4
Local Search
.182
13.3
Solving CVRP with JCell2oli
.184
13.4
New Solutions to CVRP
.185
13.5
Conclusions
.186
14
Telecommunications: Optimization of the Broadcasting
Process in MANETs
.187
14.1
The Problem
.188
14.1.1
Metropolitan Mobile Ad Hoc Networks. The Madhoc
Simulator
.188
14.1.2
Delayed Flooding with Cumulative Neighborhood
.191
14.1.3
MOPs Definition
.192
14.2
A Multi-objective cGA: cMOGA
.193
14.2.1
Dealing with Constraints
.194
14.3
Experiments
.194
14.3.1
Parameterization of cMOGA
.195
14.3.2
Madhoc Configuration
.196
14.3.3
Results for DFCNT
.198
14.4
Comparing cMOGA Against NSGA-II
.200
14.4.1
Parameterization of NSGA-II
.200
14.4.2
Discussion
.201
14.5
Conclusions
.202
15
Bioinformatics: The
DNA
Fragment Assembly Problem
. . 203
15.1
The
DNA
Fragment Assembly Problem
.204
15.2
A cMA for
DNA
Fragment Assembly Problem
.206
15.3
Results
.208
15.4
Conclusions
.210
Contents XIII
Part V Appendix
A Definition of the Benchmark Problems
.213
A.I Combinatorial Optimization Problems
.213
A.I.I COUNTSAT Problem
.213
A.I.
2
Error Correcting Codes Design Problem
-
ECC
.214
A.1.3 Frequency Modulation Sounds
-
FMS
.215
A.1.4 IsoPeak Problem
.215
A.I.
5
Maximum Cut of a Graph
-
MAXCUT
.216
A.I.
6
Massively
Multimodal
Deceptive Problem
-
MMDP
. 216
A.1.7 Minimum Tardy Task Problem
-
MTTP
.217
A.1.8 OneMax Problem
.218
A.1.9 Plateau Problem
.218
A.I.
10
P-PEAKS Problem
.218
A.I.
11
Satisfiability Problem
-
SAT
.219
A.
2
Continuous Optimization Problems
.220
A.2.1 Academic Problems
.220
A.2.2 Real World Problems
.222
A.3 Multi-objective Optimization Problems
.223
References
.225
Index
.243 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Alba, Enrique Dorronsoro, Bernabé |
| author_facet | Alba, Enrique Dorronsoro, Bernabé |
| author_role | aut aut |
| author_sort | Alba, Enrique |
| author_variant | e a ea b d bd |
| building | Verbundindex |
| bvnumber | BV035080114 |
| classification_rvk | ST 301 |
| ctrlnum | (OCoLC)254067187 (DE-599)DNB986860085 |
| 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 | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| format | Book |
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| id | DE-604.BV035080114 |
| illustrated | Illustrated |
| index_date | 2024-07-02T22:06:47Z |
| indexdate | 2024-07-09T21:21:44Z |
| institution | BVB |
| isbn | 9780387776095 9780387776101 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016748367 |
| oclc_num | 254067187 |
| open_access_boolean | |
| owner | DE-703 |
| owner_facet | DE-703 |
| physical | XIII, 245 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 | Alba, Enrique Verfasser aut Cellular genetic algorithms Enrique Alba and Bernabé Dorronsoro New York, NY Springer 2008 XIII, 245 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Operations research - computer science interfaces series 42 Genetischer Algorithmus (DE-588)4265092-6 gnd rswk-swf Genetischer Algorithmus (DE-588)4265092-6 s DE-604 Dorronsoro, Bernabé Verfasser aut Operations research - computer science interfaces series 42 (DE-604)BV012124389 42 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016748367&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Alba, Enrique Dorronsoro, Bernabé Cellular genetic algorithms Operations research - computer science interfaces series Genetischer Algorithmus (DE-588)4265092-6 gnd |
| subject_GND | (DE-588)4265092-6 |
| title | Cellular genetic algorithms |
| title_auth | Cellular genetic algorithms |
| title_exact_search | Cellular genetic algorithms |
| title_exact_search_txtP | Cellular genetic algorithms |
| title_full | Cellular genetic algorithms Enrique Alba and Bernabé Dorronsoro |
| title_fullStr | Cellular genetic algorithms Enrique Alba and Bernabé Dorronsoro |
| title_full_unstemmed | Cellular genetic algorithms Enrique Alba and Bernabé Dorronsoro |
| title_short | Cellular genetic algorithms |
| title_sort | cellular genetic algorithms |
| topic | Genetischer Algorithmus (DE-588)4265092-6 gnd |
| topic_facet | Genetischer Algorithmus |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016748367&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV012124389 |
| work_keys_str_mv | AT albaenrique cellulargeneticalgorithms AT dorronsorobernabe cellulargeneticalgorithms |