Coevolutionary computation and multiagent systems:
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Southampton
WIT Press
2012
|
| Series: | Chinese science today
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Includes bibliographical references |
| Physical Description: | 257 p. ill. |
| ISBN: | 184564638X 9781845646387 |
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MARC
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| 245 | 1 | 0 | |a Coevolutionary computation and multiagent systems |c Licheng Jiao ; Jing Liu ; Weicai Zhong |
| 264 | 1 | |a Southampton |b WIT Press |c 2012 | |
| 300 | |a 257 p. |b ill. | ||
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| 490 | 0 | |a Chinese science today | |
| 500 | |a Includes bibliographical references | ||
| 650 | 4 | |a Evolutionary computation | |
| 650 | 4 | |a Multiagent systems | |
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Record in the Search Index
| _version_ | 1804150450773032960 |
|---|---|
| adam_text | Table of
Contents
Preface
.................................................................................................................
¡x
Chapter
1
Introduction...
................................................................................... 1
1.1
Evolutionary computation
........................................................................ 1
1.1.1
Structure of evolutionary algorithms
.................................................. 1
1.1.2
Branches of evolutionary algorithms
................................................. 2
1.1.3
Evolutionary computation and complex adaptive systems
................. 3
1.2
Multiagent systems
................................................................................... 5
1.2.1
Agents
................................................................................................ 5
1.2.2
MAS
................................................................................................... 6
Chapter
2
An organizational «revolutionary algorithm for classification
.... 11
2.1
Related work
............................................................................................. 12
2.2
Organizations for classification
................................................................ 14
2.3
Fitness of organizations
............................................................................ 16
2.4
Implementation of OCEC
......................................................................... 19
2.5
Evaluation of OCEC s effectiveness
........................................................ 21
2.5.1
Multiplexer problems
......................................................................... 22
2.5.2
Experimental results
........................................................................... 23
2.6
Comparison of OCEC with available algorithms
..................................... 24
2.6.1
Comparison on UCI repository
datasets
............................................. 24
2.6.2
Comparison of OCEC with XCS on multiplexer problems
............... 28
2.6.3
Scalability of OCEC
........................................................................... 28
2.7
Practical applications of OCEC
................................................................ 31
2.7.1
Radar target recognition problems
..................................................... 31
2.7.2
Remote sensing target recognition
..................................................... 31
2.8
Conclusion
................................................................................................ 35
Chapter
3
An organizational evolutionary algorithm for satisfiability
J>7
3.1
Organizations for SAT problems
.............................................................. 40
3.2
Organizational evolutionary operators
..................................................... 41
3.3
Implementation of OEA_SAT
.................................................................. 43
3.4
Experiments
.............................................................................................. 46
3.5
Conclusion
................................................................................................ 49
Chapter
4
An organizational evolutionary algorithm for numerical
optimization
........................................................................................................ 51
4.1
Organizations for numerical optimization
................................................ 51
4.2
Evolutionary operators for organizations
................................................. 53
4.2.1
Splitting operator
................................................................................ 53
4.2.2
Annexing operator
.............................................................................. 54
4.2.3
Cooperating operator
.......................................................................... 55
4.3
Implementation of
OEA
........................................................................... 57
4.4
Convergence of
OEA
............................................................................... 58
4.5
Experiments on unconstrained optimization problems
............................. 61
4.5.1
Experimental results of
OEA
............................................................. 62
4.5.2
Comparison between
OEA
and FEP
.................................................. 62
4.5.3
Comparison between
OEA
and OGA/Q
............................................ 62
4.6
Experiments on constrained optimization problems
................................. 66
4.6.1
Experimental results of
OEA
............................................................. 66
4.6.2
Comparison between
OEA
and RY
................................................... 70
4.6.3
Comparison between
OEA
and SMES on G01 to G13
...................... 70
4.6.4
Comparison between
OEA
and
SCA
on the four engineering design
problems
...................................................................................................... 70
4.7
Parameter analyses of
OEA
...................................................................... 70
4.7.1
Effects of No on the performance of
OEA
.......................................... 77
4.7.2
Effects of AS and CS on the Performance of
OEA
............................. 78
4.7.3
Effects
oïMaxos
on the performance of
OEA
................................... 80
4.8
Conclusion
................................................................................................ 80
Chapter
5
Moving block sequence: a new VLSI floorplan representation
... 83
5.1
Related work
............................................................................................. 83
5.2
Moving block sequence representation
.................................................... 84
5.3
Algorithm transforming an MBS to a floorplan
....................................... 87
5.3.1
Information structure for rectilinear blocks
........................................ 87
5.3.2
Implementation of the algorithm.....
................................................... 88
5.3.3
An example
........................................................................................ 93
5.3.4
Conclusion
......................................................................................... 94
Chapter
6
An organizational evolutionary algorithm for general
floorplanning based on moving block sequence
.....................................____. 97
6.1
Related work
............................................................................................. 97
6.2
Organizations for floorplanning
............................................................... 99
6.3
Determining shapes and orientations of various types of blocks
.............. 100
6.4
Evolutionary operators for organizations
................................................. 102
6.5
Implementation of the algorithm
.............................................................. 104
6.6
Experiments
.............................................................................................. 106
6.6.1
Floorplanning problems with hard rectangular blocks
....................... 106
6.6.2
Floorplanning problems with soft rectangular blocks
........................ 112
6.6.3
Floorplanning problems with hybrid blocks
...................................... 116
6.6.4
Floorplanning problems with concave blocks
.................................... 119
6.7
Finding the strength of MBS-OEA
........................................................... 119
6.7.1
The MBS
............................................................................................ 122
6.7.2
The
OEA
............................................................................................ 122
6.8
Conclusion
................................................................................................ 123
Chapter
7
A multiagent genetic algorithm for global numerical
optimization
........................................................................................................ 127
7.1
Agents for numerical optimization
........................................................... 127
7.2
Four evolutionary operators for agents
..................................................... 130
7.3
Implementation of
MAGA
....................................................................... 133
7.4
Convergence of
MAGA
........................................................................... 134
7.5
Experiments
.............................................................................................. 137
7.5.1
Descriptions of the compared algorithms
........................................... 138
7.5.2
Comparison between FEP, OGA/Q, and
MAGA
on functions
with
30
dimensions
..................................................................................... 139
7.5.3
The performance of
MAGA
on functions with
20
to
1,000
dimensions
.................................................................................................. 139
7.5.4
Performance of
MAGA
on functions with
1,000
to
10,000
dimensions
.................................................................................................. 141
7.6
Experimental studies on the optimal approximation of linear systems
.... 146
7.6.
1
MAGA
for the optima] approximation of linear systems
................... 147
7.6.2
Experiments
....................................................................................... 147
7.7
Conclusion
................................................................................................ 151
Chapter
8
A Macroagent evolutionary model for decomposable function
optimization
-----......----«------.................----.—....................................—....... 155
8.1
Definition of decomposable functions
...................................................... 156
8.2
Macroagent
............................................................................................... 157
8.3
Macroagent evolutionary model
............................................................... 157
8.4
Hierarchy multiagent Genetic Algorithm
................................................. 160
8.5
Experiments on HMAGA
......................................................................... 164
8.5.1
Comparison between HMAGA and
MAGA
on Rosenbrock
function with
10
to
1,000
dimensions
......................................................... 164
8.5.2
Performance of HMAGA on the Rosenbrock function with
1,000
to
50,000
dimensions
....................................................................................... 164
8.5.3
Performance analysis of parameter
к
................................................. 167
8.6
Conclusion
................................................................................................ 168
Chapter
9
A multiagent evolutionary algorithm for combinatorial
optimization problems
—...—.................................................................—........ 169
9.1
Agents for combinatorial optimization problems
..................................... 169
9.2
Behaviors of agents
.................................................................................. 171
9.2.1
Competition behavior
......................................................................... 171
9.2.2
Self-learning behavior
........................................................................ 171
9.3
Multiagent evolutionary algorithm for combinatorial
optimization problems
.................................................................................... 173
9.3.1 Implementation
of MAEA-CmOPs
.................................................... 173
9.4
Convergence of MAEA-CmOPs
.............................................................. 174
9.5
Experiments on deceptive problems
......................................................... 177
9.5.1
Strong-linkage deceptive functions
.................................................... 179
9.5.2
Weak-linkage deceptive functions
..................................................... 181
9.5.3
Overlapping-linkage deceptive
fonctions
........................................... 182
9.6
Experiments on hierarchical problems
..................................................... 184
9.6.1
Hierarchical problems
........................................................................ 184
9.6.2
Experiments and analyses
.................................................................. 188
9.7
Conclusion
................................................................................................ 190
Chapter
IO A
multiagent evolutionary algorithm for constraint satisfaction
problems
............................................................................................................. 193
10.1
Constraint satisfaction agents
................................................................. 194
10.1.1
Constraint satisfaction problems
...................................................... 194
10.1.2
Definition of constraint satisfaction agents
...................................... 195
10.1.3
Environment of constraint satisfaction agents
.................................. 198
10.2
Behaviors of constraint satisfaction agents
............................................. 199
10.2.1
Competitive behavior
....................................................................... 199
10.2.2
Self-learning behavior
...................................................................... 200
10.2.3
Mutation behavior
............................................................................ 201
10.3
Implementation of MAEA-CSPs
............................................................ 202
10.4
Complexity analysis
............................................................................... 203
10.4.1
Space complexity of MAEA-CSPs
.................................................. 203
10.4.2
Convergence of MAEA-CSPs
.......................................................... 204
10.5
Experimental studies on non-permutation constraint
satisfaction problems
...................................................................................... 208
10.5.1
Binary constraint satisfaction problems
........................................... 208
10.5.2
Graph-coloring problems
................................................................. 210
10.6
Experimental studies on permutation constraint satisfaction problems..
217
10.6.1
л
-Queen problems
........................................................·................... 217
10.6.2
Job-shop scheduling problems
......................................................... 222
10.7
Conclusion
.............................................................................................. 225
Appendix A
15
unconstraint functions used in Chapter
4------..«--------........ 233
Appendix
В
13
constraint functions used in Chapter
4---------.----------------- 235
Appendix
С
Shape information and MBS corresponding to the results
Appendix
D
Shape information aad MBS corresponding to the results
IB
Figure
6аЗитмнганм»н.ни<>пяі«н«ніин«ни«ииІІ««м>»и»ітин<>>ми<Фм«ит.нн»н
249
Appendix
E
Shape information and MBS corresponding to the results
Ш
Coevolutionary
Computation
and Multiagent Systems
The origins of evolutionary computation can be traced back to the late
1
950s where
it remained, almost unknown, to the broader scientific community for three decades
until the
1980s
when it started to receive significant attention, as did the study of
multiagent systems (MAS). This focuses on systems in which many intelligent agents
interact with each other. Today these systems are not simply a research topic but are
also beginning to become an important subject of academic teaching and industrial
and commercial application.
Coevolutionary Computation and Multiagent Systems introduces the authors recent
work in these two new and important branches of artificial intelligence.
Tides
Ы
Related Interest
Soft Computing in Water Resources Engineering
Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms
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| any_adam_object | 1 |
| author | Jiao, Li-cheng Liu, Jing Zhong, Weicai |
| author_GND | (DE-588)132252163 (DE-588)101480065X (DE-588)1026702011 |
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| id | DE-604.BV041080011 |
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| indexdate | 2024-07-10T00:39:07Z |
| institution | BVB |
| isbn | 184564638X 9781845646387 |
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| physical | 257 p. ill. |
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| record_format | marc |
| series2 | Chinese science today |
| spelling | Jiao, Li-cheng Verfasser (DE-588)132252163 aut Coevolutionary computation and multiagent systems Licheng Jiao ; Jing Liu ; Weicai Zhong Southampton WIT Press 2012 257 p. ill. txt rdacontent n rdamedia nc rdacarrier Chinese science today Includes bibliographical references Evolutionary computation Multiagent systems Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Coevolution (DE-588)4148204-9 gnd rswk-swf Mehragentensystem (DE-588)4389058-1 gnd rswk-swf Mehragentensystem (DE-588)4389058-1 s Evolutionärer Algorithmus (DE-588)4366912-8 s Coevolution (DE-588)4148204-9 s DE-604 Liu, Jing Verfasser (DE-588)101480065X aut Zhong, Weicai Verfasser (DE-588)1026702011 aut Erscheint auch als Online-Ausgabe 978-1-84564-639-4 Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026056818&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026056818&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Jiao, Li-cheng Liu, Jing Zhong, Weicai Coevolutionary computation and multiagent systems Evolutionary computation Multiagent systems Evolutionärer Algorithmus (DE-588)4366912-8 gnd Coevolution (DE-588)4148204-9 gnd Mehragentensystem (DE-588)4389058-1 gnd |
| subject_GND | (DE-588)4366912-8 (DE-588)4148204-9 (DE-588)4389058-1 |
| title | Coevolutionary computation and multiagent systems |
| title_auth | Coevolutionary computation and multiagent systems |
| title_exact_search | Coevolutionary computation and multiagent systems |
| title_full | Coevolutionary computation and multiagent systems Licheng Jiao ; Jing Liu ; Weicai Zhong |
| title_fullStr | Coevolutionary computation and multiagent systems Licheng Jiao ; Jing Liu ; Weicai Zhong |
| title_full_unstemmed | Coevolutionary computation and multiagent systems Licheng Jiao ; Jing Liu ; Weicai Zhong |
| title_short | Coevolutionary computation and multiagent systems |
| title_sort | coevolutionary computation and multiagent systems |
| topic | Evolutionary computation Multiagent systems Evolutionärer Algorithmus (DE-588)4366912-8 gnd Coevolution (DE-588)4148204-9 gnd Mehragentensystem (DE-588)4389058-1 gnd |
| topic_facet | Evolutionary computation Multiagent systems Evolutionärer Algorithmus Coevolution Mehragentensystem |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026056818&sequence=000003&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=026056818&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT jiaolicheng coevolutionarycomputationandmultiagentsystems AT liujing coevolutionarycomputationandmultiagentsystems AT zhongweicai coevolutionarycomputationandmultiagentsystems |