Spatially structured evolutionary algorithms: artificial evolution in space and time
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2005
|
| Series: | Natural computing series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIII, 192 S. Ill., graph. Darst. |
| ISBN: | 9783540241935 3540241930 |
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MARC
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| 100 | 1 | |a Tomassini, Marco |e Verfasser |0 (DE-588)122948084 |4 aut | |
| 245 | 1 | 0 | |a Spatially structured evolutionary algorithms |b artificial evolution in space and time |c Marco Tomassini |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2005 | |
| 300 | |a XIII, 192 S. |b Ill., graph. Darst. | ||
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| 650 | 4 | |a Evolutionary programming (Computer science) | |
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Record in the Search Index
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| adam_text | Contents
Setting the Stage for Structured Populations
.............. 1
1.1
Useful Definitions for Graphs
............................. 2
1.2
Main Graph Structures of Populations
..................... 5
1.2.1
Island or Multipopulation Models
................... 6
1.2.2
Cellular Models
................................... 7
1.2.3
Other Topologies
.................................. 8
Island Models
............................................. 11
2.1
History and Background
.................................. 11
2.2
Homogeneous and Heterogeneous Islands
................... 14
2.3
Theoretical Results
...................................... 14
2.4
Asynchronous Islands
.................................... 17
Island Models: Empirical Properties
....................... 19
3.1
An Experimental Investigation
............................ 19
3.2
Description of the Test Problems
.......................... 19
3.3
Multipopulation GP Parameters
........................... 20
3.4
Performance Measures and Statistics
....................... 21
3.5
Number of
Subpopulations
and Their Size
.................. 23
3.5.1
Isolated Populations vs. Standard GP
................ 23
3.5.2
Communicating Islands vs. Standard GP
............. 27
3.6
Comparing Communication Topologies
..................... 30
3.7
Migration Parameters
.................................... 33
3.8
Summary of Case Study
.................................. 37
3.9
The Role of Diversity
.................................... 37
3.9.1
Diversity Measures
................................ 38
3.9/2
Experimental Results
.............................. 40
3.9.3
Summary
......................................... 44
3.1.0
Asynchronous Island Models
.............................. 44
3.10.1
Experimental Results
.............................. 46
4
Lattice Cellular Models
.................................... 53
4.1
Takeover Time
.......................................... 54
4.2
Synchronous cEAs
....................................... 55
4.3
The Time Dimension: Asynchronous cEAs
.................. 57
4.4
Models and Their Validation
.............................. 59
4.5
Mathematical Models
.................................... 59
4.5.1
Limitations of Logistic Modeling
.................... 60
4.5.2
The Ring Structure
................................ 62
4.5.3
The Torus Structure
............................... 66
4.6
Experimental Validation
.................................. 70
4.6.1
Ring Structure
.................................... 70
4.6.2
Torus Structure
................................... 73
4.7
Rectangular Toroidal Structures
........................... 76
4.7.1
Validation of the Rectangular Toroidal Models
........ 78
4.8
Varying the Radius
...................................... 79
4.9
Summary
............................................... 80
5
Lattice Cellular Models: Empirical Properties
............. 85
5.1
cGA Case Study
........................................ 85
5.2
Varying the Lattice Shape
................................ 86
5.3
Selection Pressure. Grid Shape and Time
.................. . 87
5.4
Test Suite
.............................................. 88
5.4.1
Discrete Optimization Problems
..............:...... 90
5.4.2
Experimental Analysis
............................. 93
5.4.3
Continuous Optimization Problems
.................. 96
5.4.4
Experimental Analysis
.............................. 97
5.5
Cellular Genetic Programming
............................100
5.5.1
Experimental Results
..............................100
5.5.2
Fitness Evolution
..................................100
5.5.3
Diversity Evolution
................................101
5.6
Summary
...............................................104
6
Random and Irregular Cellular Populations
................107
6.1
Random Graphs
.........................................108
6.2
Selection Intensity in Random Cellular Populations
..........109
6.3
Experimental Results
....................................
Ill
6.4
Small-World Networks
...................................113
6.4.1
Some Graph Statistics
.............................116
6.4.2
The Watts Strogatz Model
.........................118
6.4.3
The
Bara basi-
Albert Model
........................119
6.5
Selection Intensity in Small-World Networks
................120
6.5.1
Watts-Strogatz Model
............................. 120
6.5.2
Barabást
Albert Model
............................122
6.6
Summary
...............................................124
7
Coevolutionary Structured Models
........................125
7.
L W
li
at Is Coevolutiou?
....................................125
7.2
Cooperative Cocvolution
.................................126
7.3
Competitive Coevolution: Hosts and Parasites
...............127
7.4
A Case Study: Coevolution of Cellular Automata
............130
7.4.1
Cellular Automata
.................................131
7.4.2
The Majority Task
................................131
7.5
Artificial Evolution of CAs for the Majority Task
............ 133
7.5.1
Coevolving Uniform CAs for the Majority Task
.......134
7.5.2
Coevolving Nonuniform CAs for the Majority Task
.... 137
7.5.3
Results for the Density Task
........................139
7.6
Summan-
...............................................141
8
Some Nonconventional Models
.............................143
8.1
Nonconventional Island Models
............................144
8.1.1
The Injection Island Model
. . .......................144
8.1.2
Islands with Variable Population Size
................145
8.2
Nonconventional Cellular Models
..........................151
8.2.1
The Patchwork Model
.............................152
8.2.2
Dynamic Neighborhoods in Cellular Evolution Stxategiesl53
A Implementation Notes
.....................................157
A.l Computing Environment
.................................158
A.
1.1
MPI
.............................................158
A.
2
Implementation of Island EAs
.............................159
A.
2.1
Synchronous Islands
...............................159
A.
2.2
Asynchronous Islands
..............................165
A
.2,3
Summary
.........................................168
A.3 Implementation of Lattice Cellular EAs
....................168
A
.3.1
Synchronous Cellular EAs
..........................168
A.
3.2
Asynchronous Cellular EAs
.........................170
A.
4
Performance Measures and Speedup
.......................170
A.
5
A Remark on Pseudorandom Number Generators
............173
References
.....................................................177
Index
..........................................................189
|
| adam_txt |
Contents
Setting the Stage for Structured Populations
. 1
1.1
Useful Definitions for Graphs
. 2
1.2
Main Graph Structures of Populations
. 5
1.2.1
Island or Multipopulation Models
. 6
1.2.2
Cellular Models
. 7
1.2.3
Other Topologies
. 8
Island Models
. 11
2.1
History and Background
. 11
2.2
Homogeneous and Heterogeneous Islands
. 14
2.3
Theoretical Results
. 14
2.4
Asynchronous Islands
. 17
Island Models: Empirical Properties
. 19
3.1
An Experimental Investigation
. 19
3.2
Description of the Test Problems
. 19
3.3
Multipopulation GP Parameters
. 20
3.4
Performance Measures and Statistics
. 21
3.5
Number of
Subpopulations
and Their Size
. 23
3.5.1
Isolated Populations vs. Standard GP
. 23
3.5.2
Communicating Islands vs. Standard GP
. 27
3.6
Comparing Communication Topologies
. 30
3.7
Migration Parameters
. 33
3.8
Summary of Case Study
. 37
3.9
The Role of Diversity
. 37
3.9.1
Diversity Measures
. 38
3.9/2
Experimental Results
. 40
3.9.3
Summary
. 44
3.1.0
Asynchronous Island Models
. 44
3.10.1
Experimental Results
. 46
4
Lattice Cellular Models
. 53
4.1
Takeover Time
. 54
4.2
Synchronous cEAs
. 55
4.3
The Time Dimension: Asynchronous cEAs
. 57
4.4
Models and Their Validation
. 59
4.5
Mathematical Models
. 59
4.5.1
Limitations of Logistic Modeling
. 60
4.5.2
The Ring Structure
. 62
4.5.3
The Torus Structure
. 66
4.6
Experimental Validation
. 70
4.6.1
Ring Structure
. 70
4.6.2
Torus Structure
. 73
4.7
Rectangular Toroidal Structures
. 76
4.7.1
Validation of the Rectangular Toroidal Models
. 78
4.8
Varying the Radius
. 79
4.9
Summary
. 80
5
Lattice Cellular Models: Empirical Properties
. 85
5.1
cGA Case Study
. 85
5.2
Varying the Lattice Shape
. 86
5.3
Selection Pressure. Grid Shape and Time
. . 87
5.4
Test Suite
. 88
5.4.1
Discrete Optimization Problems
.:. 90
5.4.2
Experimental Analysis
. 93
5.4.3
Continuous Optimization Problems
. 96
5.4.4
Experimental Analysis
. 97
5.5
Cellular Genetic Programming
.100
5.5.1
Experimental Results
.100
5.5.2
Fitness Evolution
.100
5.5.3
Diversity Evolution
.101
5.6
Summary
.104
6
Random and Irregular Cellular Populations
.107
6.1
Random Graphs
.108
6.2
Selection Intensity in Random Cellular Populations
.109
6.3
Experimental Results
.
Ill
6.4
Small-World Networks
.113
6.4.1
Some Graph Statistics
.116
6.4.2
The Watts Strogatz Model
.118
6.4.3
The
Bara basi-
Albert Model
.119
6.5
Selection Intensity in Small-World Networks
.120
6.5.1
Watts-Strogatz Model
. 120
6.5.2
Barabást
Albert Model
.122
6.6
Summary
.124
7
Coevolutionary Structured Models
.125
7.
L W
li
at Is Coevolutiou?
.125
7.2
Cooperative Cocvolution
.126
7.3
Competitive Coevolution: Hosts and Parasites
.127
7.4
A Case Study: Coevolution of Cellular Automata
.130
7.4.1
Cellular Automata
.131
7.4.2
The Majority Task
.131
7.5
Artificial Evolution of CAs for the Majority Task
. 133
7.5.1
Coevolving Uniform CAs for the Majority Task
.134
7.5.2
Coevolving Nonuniform CAs for the Majority Task
. 137
7.5.3
Results for the Density Task
.139
7.6
Summan-
.141
8
Some Nonconventional Models
.143
8.1
Nonconventional Island Models
.144
8.1.1
The Injection Island Model
. . .144
8.1.2
Islands with Variable Population Size
.145
8.2
Nonconventional Cellular Models
.151
8.2.1
The Patchwork Model
.152
8.2.2
Dynamic Neighborhoods in Cellular Evolution Stxategiesl53
A Implementation Notes
.157
A.l Computing Environment
.158
A.
1.1
MPI
.158
A.
2
Implementation of Island EAs
.159
A.
2.1
Synchronous Islands
.159
A.
2.2
Asynchronous Islands
.165
A
.2,3
Summary
.168
A.3 Implementation of Lattice Cellular EAs
.168
A
.3.1
Synchronous Cellular EAs
.168
A.
3.2
Asynchronous Cellular EAs
.170
A.
4
Performance Measures and Speedup
.170
A.
5
A Remark on Pseudorandom Number Generators
.173
References
.177
Index
.189 |
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| id | DE-604.BV021254668 |
| illustrated | Illustrated |
| index_date | 2024-07-02T13:40:12Z |
| indexdate | 2024-07-09T20:33:57Z |
| institution | BVB |
| isbn | 9783540241935 3540241930 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014575989 |
| oclc_num | 62902448 |
| open_access_boolean | |
| owner | DE-703 DE-824 DE-91G DE-BY-TUM DE-384 |
| owner_facet | DE-703 DE-824 DE-91G DE-BY-TUM DE-384 |
| physical | XIII, 192 S. Ill., graph. Darst. |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Springer |
| record_format | marc |
| series2 | Natural computing series |
| spelling | Tomassini, Marco Verfasser (DE-588)122948084 aut Spatially structured evolutionary algorithms artificial evolution in space and time Marco Tomassini Berlin [u.a.] Springer 2005 XIII, 192 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Natural computing series Evolutionary computation Evolutionary programming (Computer science) Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 s DE-604 Digitalisierung UB Augsburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014575989&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Tomassini, Marco Spatially structured evolutionary algorithms artificial evolution in space and time Evolutionary computation Evolutionary programming (Computer science) Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| subject_GND | (DE-588)4366912-8 |
| title | Spatially structured evolutionary algorithms artificial evolution in space and time |
| title_auth | Spatially structured evolutionary algorithms artificial evolution in space and time |
| title_exact_search | Spatially structured evolutionary algorithms artificial evolution in space and time |
| title_exact_search_txtP | Spatially structured evolutionary algorithms artificial evolution in space and time |
| title_full | Spatially structured evolutionary algorithms artificial evolution in space and time Marco Tomassini |
| title_fullStr | Spatially structured evolutionary algorithms artificial evolution in space and time Marco Tomassini |
| title_full_unstemmed | Spatially structured evolutionary algorithms artificial evolution in space and time Marco Tomassini |
| title_short | Spatially structured evolutionary algorithms |
| title_sort | spatially structured evolutionary algorithms artificial evolution in space and time |
| title_sub | artificial evolution in space and time |
| topic | Evolutionary computation Evolutionary programming (Computer science) Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| topic_facet | Evolutionary computation Evolutionary programming (Computer science) Evolutionärer Algorithmus |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014575989&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT tomassinimarco spatiallystructuredevolutionaryalgorithmsartificialevolutioninspaceandtime |