Advances in evolutionary algorithms: theory, design and practice
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg
Springer
2006
|
| Series: | Studies in computational intelligence
18 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XV, 171 S. graph. Darst. |
| ISBN: | 9783540317586 3540317589 |
Staff View
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| 100 | 1 | |a Ahn, Chang Wook |e Verfasser |0 (DE-588)13143005X |4 aut | |
| 245 | 1 | 0 | |a Advances in evolutionary algorithms |b theory, design and practice |c Chang Wook Ahn |
| 264 | 1 | |a Berlin ; Heidelberg |b Springer |c 2006 | |
| 300 | |a XV, 171 S. |b graph. Darst. | ||
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| 490 | 1 | |a Studies in computational intelligence |v 18 | |
| 650 | 4 | |a Evolutionary programming (Computer science) | |
| 650 | 4 | |a Genetic algorithms | |
| 650 | 0 | 7 | |a Evolutionärer Algorithmus |0 (DE-588)4366912-8 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Evolutionärer Algorithmus |0 (DE-588)4366912-8 |D s |
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Record in the Search Index
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| adam_text | CHANG WOOK AHN
ADVANCE
S IN EVOLUTIONAR
Y
ALGORITHM
S
THEORY, DESIGN AND PRACTICE
SPRINGE
R
CONTENT
S
1 INTRODUCTIO
N 1
1.1 MOTIVATIO
N 2
1.2 OBJECTIVC
S 3
1.3 OUTLIN
E
4
2 PRACTICA
L GENETI
C ALGORITHM
S 7
2.1 GCIIETIC ALGOIITHMK: SIMPLE T
O OOMPETEN
T 7
2.1.1 OVERVIEW OF GENETI
C ALGORITHM
S 7
2.1.2 DCSIGN-DOCOMPOSITION TLIEORY 9
2.2 PRACTICA
L DESIGN GUIDELINES 11
2.3 PRACTICA
L POPULATION-SIZIN
G MODEL , 14
2.3.1 REVIEW OF POPULATION-SIZIN
G MODELS 14
2.3.2 HARIK
S DECISION MODE
L 15
2.3.3 PRACTICA
L DECISION MODEL 15
2.3.4 PRACTICA
L POPULATION-SIZIN
G MODEL 17
2.3.5 EXPCRIMENTA
L VERIFICATION 19
2.4 SUMMAR
Y 22
3 REAL-WORL
D APPLICATION
: ROUTIN
G PROBLE
M 23
3.1 MOTIVATIO
N 23
3.2 EXISTIN
G GA-BASOD APPROACHE
S 24
3.3 PROPOSC
D GA-BASCD ROUTIN
G ALGORITLIM 26
3.3.1 CHROMOSOM
E REPRESENTATIO
N 26
3.3.2 POPULATIO
N IIIITIALIZATIO
N 27
3.3.3 FITNES
S FUNCTIO
N 28
3.3.1 GENETI
C OPERATOR
S 28
3.3.5 RCPAI
R FUNCTIO
N 31
3.3.6 POPULATIO
N SIZE 33
3.4 EXPERIMENT
S AN
D DISCUSSION 33
3.4.1 R.CSULTS FOR A FIXED NETWORK WIT
H 20 NODE
S 33
3.4.2 R.EWULTS FOR RANDO
M NETWORKS 35
XIV CONTENT
S
3.4.
3 EXPERIMENTA
L VCIIFICATIO
N OF TH
C POPULATIOMSIZHI
G
MODE
L 3
9
3.5 SUMMAR
Y 4
2
4 ELITIS
T COMPAC
T GENETI
C ALGORITHM
S 4
5
4.
1 A FAMIL
Y OF COMPACT
. GCNCTI
C ALGORITHM
S 4
6
4.
2 COMPAC
T GENETI
C ALGORITH
M AN
D ELITIS
M 4
8
4.2.
1 COMPAC
T GENETI
C ALGORITH
M 4
8
4.2.
2 ELITIS
M 4
9
4.
3 ELITISM-BA.SC
D COMPAC
T GENETI
C ALGORITHM
S 5
0
4.3.
1 PERSISTEN
T ELITIS
T COMPAC
T GENETI
C ALGORITH
M 50
4.3.
2 NONPERSISTEII
T ELIL.IS
T COMPAC
T GCNCTI
C ALGORITH
M 5
3
4.
4 SPCEDN
P MODE
L 5
5
1.5 EXPERIMENTA
L RESULT
S AN
D DISCUSSIO
N 5
9
4.5.
1 RESULT
S FOR TH
C PROBLEM
S IIIVOLVIN
G LOWC
R ORDE
R BB
S . . 6
0
1.5.2 RESULT
S FOR TH
C PROBLEM
S INVOLVIN
G HIGHE
R ORDE
R BB
S . . 6
4
4.5.
3 RESULT
S FOR COIITINUOU
S AN
D MULTIMODA
L PROBLEM
S 68
4.5.
4 COMPARISO
N RESULT
S WIT
H EVOLUTIOIIAR
Y STRATEGIE
S 7
3
4.5.
5 EFFECT
S OF TH
E SCOP
E OF INHERITANE
E 75
4.5.
6 REAL-WORL
D APPLICATIONS
: ISIN
G SPIN-GLASSE
S (ISG
)
SYSTEM
S G
0
4.
6 SUMMAR
Y $[
5 REAL-CODC
D BAYESIA
N OPTIMIZATIO
N ALGORITH
M 8
5
5.1 ESTIRNATIO
N OF DISTRIBUTIO
N ALGORITHM
S 8
6
5.2 HCAL-CODE
D BAYESIA
N OPTIMIZATIO
N ALGORITH
M 8
9
5.
3 LEARNIN
G OF PROBABILISTI
C MODEI
S 9
1
5.3.
1 MODE
L SELECTIO
N 9
1
5.3.
2 MODE
L EITTIN
G 94
5.4 SAMPLIN
G OF PROBABILISTI
C MODEL
S 9
9
5.5 SCALABILIT
Y ANALYSI
S 9
9
5.5.
1 PRELIMINARIE
S 9
9
5.5.
2 POPULATIO
N COMPLEXIT
Y 10
1
5.5.
3 CONVERGEUE
E TIM
E COMPLEXIT
Y 10
8
5.5.
4 SCALABILIT
Y OF RBO
A 109
5.6 REABVALUE
D TES
T PROBLEM
S 10
9
5.6.1 DEEOMPOSABL
E PROBLEM
S 109
5.6.
2 TRADITIONA
L OPTIMIZATIO
N BCII(4IMARK
S H
L
5.
7 EXPERIMENTA
L RESULT
S AN
D DISCUSSIO
N 11
3
5.7.
1 EXPERIMEN
T SETU
P 11
3
5.7.
2 RESULT
S FOR TH
E RBO
A PERFORMANC
E 114
5.7.
3 VERIFICATIO
N OF RBO
A SCALABILIT
Y 120
5.8 SUMMAR
Y 12
3
CONTENT.S XV
6 MULTIOBJECTIV
E REAL-CODE
D BAYESIA
N OPTIMIZATIO
N
ALGORITH
M 12
J
6.1 MULTIOBJECTIV
E OPTIMIZATIO
N 126
6.2 MULTIOBJECTIV
E GENETI
C AN
D KVOHITIOIIARY ALGORITHM
S 127
6.3 MULTIOBJECTIV
E REAL-CODE
D
BAYESIAN OPTIMIZATIO
N ALGORITH
M 129
6.4 SEFECTIOII STRATEG
Y . 131
6.4.1 RANKIN
G 131
6.4.2 ADAPTIV
E SHARIN
G 132
6.4.3 DYNAMI
C CIOWDIN
G 133
6.1.4 FITNES
S ASSIGNMENT 135
6.4.5 ELITIS
M 136
6.5 REAL-VALUCD MULTIOBJECTIV
E OPTIMIZATIO
N PROBLEM
S 136
6.5.1 DCCOMPOSABL
C MULTIOBJECTIV
E OPTIMIZATIO
N PROBLEM
S . . 136
6.5.2 TRADITIONA
L MULTIOBJECTIV
E OPTIMIZATIO
N PROBLEM
S 139
6.6 EXPERIMENTA
L RESULT
S AN
D DISCUSSION 140
6.6.1 PERFORMANCE MEASURC
S 140
6.6.2 EXPERIMEN
T SETU
P 142
6.6.3 RESULT
S AN
D DISCUSSION 143
6.7 SUMMAR
Y 151
7 CONCLUSION
S 153
7.1 SUMMAR
Y 153
7.2 FUTUR
E WORK 155
7.2.1 LNCORPORATIN
G EMEIENCY-ENHANCEMEN
T TCCHNIQUES 155
7.2.2 CHALLENGIN
G L
O HIERARCHICA
L DIFFIEULTY 156
7.3 CONCLUDIN
G REMARK
S 156
REFCRENCE
S 159
INDE
X
167
|
| adam_txt |
CHANG WOOK AHN
ADVANCE
S IN EVOLUTIONAR
Y
ALGORITHM
S
THEORY, DESIGN AND PRACTICE
SPRINGE
R
CONTENT
S
1 INTRODUCTIO
N 1
1.1 MOTIVATIO
N 2
1.2 OBJECTIVC
S 3
1.3 OUTLIN
E
4
2 PRACTICA
L GENETI
C ALGORITHM
S 7
2.1 GCIIETIC ALGOIITHMK: SIMPLE T
O OOMPETEN
T 7
2.1.1 OVERVIEW OF GENETI
C ALGORITHM
S 7
2.1.2 DCSIGN-DOCOMPOSITION TLIEORY 9
2.2 PRACTICA
L DESIGN GUIDELINES 11
2.3 PRACTICA
L POPULATION-SIZIN
G MODEL , 14
2.3.1 REVIEW OF POPULATION-SIZIN
G MODELS 14
2.3.2 HARIK'
S DECISION MODE
L 15
2.3.3 PRACTICA
L DECISION MODEL 15
2.3.4 PRACTICA
L POPULATION-SIZIN
G MODEL 17
2.3.5 EXPCRIMENTA
L VERIFICATION 19
2.4 SUMMAR
Y 22
3 REAL-WORL
D APPLICATION
: ROUTIN
G PROBLE
M 23
3.1 MOTIVATIO
N 23
3.2 EXISTIN
G GA-BASOD APPROACHE
S 24
3.3 PROPOSC
D GA-BASCD ROUTIN
G ALGORITLIM 26
3.3.1 CHROMOSOM
E REPRESENTATIO
N 26
3.3.2 POPULATIO
N IIIITIALIZATIO
N 27
3.3.3 FITNES
S FUNCTIO
N 28
3.3.1 GENETI
C OPERATOR
S 28
3.3.5 RCPAI
R FUNCTIO
N 31
3.3.6 POPULATIO
N SIZE 33
3.4 EXPERIMENT
S AN
D DISCUSSION 33
3.4.1 R.CSULTS FOR A FIXED NETWORK WIT
H 20 NODE
S 33
3.4.2 R.EWULTS FOR RANDO
M NETWORKS 35
XIV CONTENT
S
3.4.
3 EXPERIMENTA
L VCIIFICATIO
N OF TH
C POPULATIOMSIZHI
G
MODE
L 3
9
3.5 SUMMAR
Y 4
2
4 ELITIS
T COMPAC
T GENETI
C ALGORITHM
S 4
5
4.
1 A FAMIL
Y OF COMPACT
. GCNCTI
C ALGORITHM
S 4
6
4.
2 COMPAC
T GENETI
C ALGORITH
M AN
D ELITIS
M 4
8
4.2.
1 COMPAC
T GENETI
C ALGORITH
M 4
8
4.2.
2 ELITIS
M 4
9
4.
3 ELITISM-BA.SC
D COMPAC
T GENETI
C ALGORITHM
S 5
0
4.3.
1 PERSISTEN
T ELITIS
T COMPAC
T GENETI
C ALGORITH
M 50
4.3.
2 NONPERSISTEII
T ELIL.IS
T COMPAC
T GCNCTI
C ALGORITH
M 5
3
4.
4 SPCEDN
P MODE
L 5
5
1.5 EXPERIMENTA
L RESULT
S AN
D DISCUSSIO
N 5
9
4.5.
1 RESULT
S FOR TH
C PROBLEM
S IIIVOLVIN
G LOWC
R ORDE
R BB
S . . 6
0
1.5.2 RESULT
S FOR TH
C PROBLEM
S INVOLVIN
G HIGHE
R ORDE
R BB
S . . 6
4
4.5.
3 RESULT
S FOR COIITINUOU
S AN
D MULTIMODA
L PROBLEM
S 68
4.5.
4 COMPARISO
N RESULT
S WIT
H EVOLUTIOIIAR
Y STRATEGIE
S 7
3
4.5.
5 EFFECT
S OF TH
E SCOP
E OF INHERITANE
E 75
4.5.
6 REAL-WORL
D APPLICATIONS
: ISIN
G SPIN-GLASSE
S (ISG
)
SYSTEM
S G
0
4.
6 SUMMAR
Y $[
5 REAL-CODC
D BAYESIA
N OPTIMIZATIO
N ALGORITH
M 8
5
5.1 ESTIRNATIO
N OF DISTRIBUTIO
N ALGORITHM
S 8
6
5.2 HCAL-CODE
D BAYESIA
N OPTIMIZATIO
N ALGORITH
M 8
9
5.
3 LEARNIN
G OF PROBABILISTI
C MODEI
S 9
1
5.3.
1 MODE
L SELECTIO
N 9
1
5.3.
2 MODE
L EITTIN
G 94
5.4 SAMPLIN
G OF PROBABILISTI
C MODEL
S 9
9
5.5 SCALABILIT
Y ANALYSI
S 9
9
5.5.
1 PRELIMINARIE
S 9
9
5.5.
2 POPULATIO
N COMPLEXIT
Y 10
1
5.5.
3 CONVERGEUE
E TIM
E COMPLEXIT
Y 10
8
5.5.
4 SCALABILIT
Y OF RBO
A 109
5.6 REABVALUE
D TES
T PROBLEM
S 10
9
5.6.1 DEEOMPOSABL
E PROBLEM
S 109
5.6.
2 TRADITIONA
L OPTIMIZATIO
N BCII(4IMARK
S H
L
5.
7 EXPERIMENTA
L RESULT
S AN
D DISCUSSIO
N 11
3
5.7.
1 EXPERIMEN
T SETU
P 11
3
5.7.
2 RESULT
S FOR TH
E RBO
A PERFORMANC
E 114
5.7.
3 VERIFICATIO
N OF RBO
A SCALABILIT
Y 120
5.8 SUMMAR
Y 12
3
CONTENT.S XV
6 MULTIOBJECTIV
E REAL-CODE
D BAYESIA
N OPTIMIZATIO
N
ALGORITH
M 12
J
6.1 MULTIOBJECTIV
E OPTIMIZATIO
N 126
6.2 MULTIOBJECTIV
E GENETI
C AN
D KVOHITIOIIARY ALGORITHM
S 127
6.3 MULTIOBJECTIV
E REAL-CODE
D
BAYESIAN OPTIMIZATIO
N ALGORITH
M 129
6.4 SEFECTIOII STRATEG
Y . "131
6.4.1 RANKIN
G 131
6.4.2 ADAPTIV
E SHARIN
G 132
6.4.3 DYNAMI
C CIOWDIN
G 133
6.1.4 FITNES
S ASSIGNMENT 135
6.4.5 ELITIS
M 136
6.5 REAL-VALUCD MULTIOBJECTIV
E OPTIMIZATIO
N PROBLEM
S 136
6.5.1 DCCOMPOSABL
C MULTIOBJECTIV
E OPTIMIZATIO
N PROBLEM
S . . 136
6.5.2 TRADITIONA
L MULTIOBJECTIV
E OPTIMIZATIO
N PROBLEM
S 139
6.6 EXPERIMENTA
L RESULT
S AN
D DISCUSSION 140
6.6.1 PERFORMANCE MEASURC
S 140
6.6.2 EXPERIMEN
T SETU
P 142
6.6.3 RESULT
S AN
D DISCUSSION 143
6.7 SUMMAR
Y 151
7 CONCLUSION
S 153
7.1 SUMMAR
Y 153
7.2 FUTUR
E WORK 155
7.2.1 LNCORPORATIN
G EMEIENCY-ENHANCEMEN
T TCCHNIQUES 155
7.2.2 CHALLENGIN
G L
O HIERARCHICA
L DIFFIEULTY 156
7.3 CONCLUDIN
G REMARK
S 156
REFCRENCE
S 159
INDE
X
167 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Ahn, Chang Wook |
| author_GND | (DE-588)13143005X |
| author_facet | Ahn, Chang Wook |
| author_role | aut |
| author_sort | Ahn, Chang Wook |
| author_variant | c w a cw cwa |
| building | Verbundindex |
| bvnumber | BV021590106 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.618 |
| callnumber-search | QA76.618 |
| callnumber-sort | QA 276.618 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | ST 134 ST 301 |
| ctrlnum | (OCoLC)65207841 (DE-599)BVBBV021590106 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
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| id | DE-604.BV021590106 |
| illustrated | Illustrated |
| index_date | 2024-07-02T14:44:11Z |
| indexdate | 2024-07-09T20:39:22Z |
| institution | BVB |
| isbn | 9783540317586 3540317589 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014805604 |
| oclc_num | 65207841 |
| open_access_boolean | |
| owner | DE-703 DE-83 |
| owner_facet | DE-703 DE-83 |
| physical | XV, 171 S. graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Ahn, Chang Wook Verfasser (DE-588)13143005X aut Advances in evolutionary algorithms theory, design and practice Chang Wook Ahn Berlin ; Heidelberg Springer 2006 XV, 171 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 18 Evolutionary programming (Computer science) Genetic algorithms Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 s DE-604 Studies in computational intelligence 18 (DE-604)BV020822171 18 text/html http://deposit.dnb.de/cgi-bin/dokserv?id=2754959&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014805604&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ahn, Chang Wook Advances in evolutionary algorithms theory, design and practice Studies in computational intelligence Evolutionary programming (Computer science) Genetic algorithms Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| subject_GND | (DE-588)4366912-8 |
| title | Advances in evolutionary algorithms theory, design and practice |
| title_auth | Advances in evolutionary algorithms theory, design and practice |
| title_exact_search | Advances in evolutionary algorithms theory, design and practice |
| title_exact_search_txtP | Advances in evolutionary algorithms theory, design and practice |
| title_full | Advances in evolutionary algorithms theory, design and practice Chang Wook Ahn |
| title_fullStr | Advances in evolutionary algorithms theory, design and practice Chang Wook Ahn |
| title_full_unstemmed | Advances in evolutionary algorithms theory, design and practice Chang Wook Ahn |
| title_short | Advances in evolutionary algorithms |
| title_sort | advances in evolutionary algorithms theory design and practice |
| title_sub | theory, design and practice |
| topic | Evolutionary programming (Computer science) Genetic algorithms Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| topic_facet | Evolutionary programming (Computer science) Genetic algorithms Evolutionärer Algorithmus |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=2754959&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=014805604&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT ahnchangwook advancesinevolutionaryalgorithmstheorydesignandpractice |