Experimental research in evolutionary computation: the new experimentalism ; with 36 tables
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2006
|
| Schriftenreihe: | Natural computing series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 185 - 202 |
| Beschreibung: | XIV, 214 S. graph. Darst. 24 cm |
| ISBN: | 9783540320265 3540320261 |
Internformat
MARC
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| 015 | |a 06,A18,0913 |2 dnb | ||
| 016 | 7 | |a 979049458 |2 DE-101 | |
| 020 | |a 9783540320265 |c Pp. : EUR 53.45 |9 978-3-540-32026-5 | ||
| 020 | |a 3540320261 |c Pp. : EUR 53.45 |9 3-540-32026-1 | ||
| 035 | |a (OCoLC)181555471 | ||
| 035 | |a (DE-599)BVBBV021686692 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
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| 044 | |a gw |c XA-DE | ||
| 049 | |a DE-83 | ||
| 084 | |a ST 134 |0 (DE-625)143590: |2 rvk | ||
| 084 | |a 004 |2 sdnb | ||
| 084 | |a 510 |2 sdnb | ||
| 100 | 1 | |a Bartz-Beielstein, Thomas |d 1966- |e Verfasser |0 (DE-588)124999476 |4 aut | |
| 245 | 1 | 0 | |a Experimental research in evolutionary computation |b the new experimentalism ; with 36 tables |c Thomas Bartz-Beielstein |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2006 | |
| 300 | |a XIV, 214 S. |b graph. Darst. |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Natural computing series | |
| 500 | |a Literaturverz. S. 185 - 202 | ||
| 650 | 4 | |a Evolutionary computation |x Research | |
| 650 | 4 | |a Evolutionary programming (Computer science) |x Research | |
| 650 | 4 | |a Research |x Methodology | |
| 650 | 0 | 7 | |a Evolutionärer Algorithmus |0 (DE-588)4366912-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Versuchsplanung |0 (DE-588)4078859-3 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Evolutionärer Algorithmus |0 (DE-588)4366912-8 |D s |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-014900811 | ||
Datensatz im Suchindex
| _version_ | 1804135510697836544 |
|---|---|
| adam_text | CONTENTS
PART
I
BASICS
1
RESEARCH
IN
EVOLUTIONARY
COMPUTATION
....................
3
1.1
RESEARCHPROBLEMS
......................................
3
1.2
BACKGROUND.............................................
4
1.2.1
EFFECTIVEAPPROACHES...............................
5
1.2.2
META-ALGORITHMS..................................
6
1.2.3
ACADEMICAPPROACHES..............................
6
1.2.4
APPROACHESWITHDIFFERENTGOALS
....................
7
1.3
COMMON
GROUNDS:
OPTIMIZATION
RUNS
TREATED
AS
EXPERIMENTS
8
1.3.1
WIND
TUNNELS.....................................
9
1.3.2
THENEWEXPERIMENTALISM..........................
10
1.4
OVERVIEWOFTHEREMAININGCHAPTERS.......................
10
2
THE
NEW
EXPERIMENTALISM
.................................
13
2.1
DEMONSTRATINGANDUNDERSTANDING.........................
13
2.1.1
WHY
DO
WE
NEED
EXPERIMENTS
IN
COMPUTER
SCIENCE?..
14
2.1.2
IMPORTANTRESEARCHQUESTIONS
......................
17
2.2
EXPERIMENTALALGORITHMICS
...............................
17
2.2.1
PREEXPERIMENTALPLANNING..........................
17
2.2.2
GUIDELINES
FROM
EXPERIMENTAL
ALGORITHMICS...........
18
2.3
OBSERVATIONALDATAANDNOISE.............................
19
2.4
MODELS.................................................
20
2.5
THENEWEXPERIMENTALISM................................
21
2.5.1
MAYO SMODELSOFSTATISTICALTESTING.................
23
2.5.2
NEYMAN-PEARSONPHILOSOPHY........................
23
2.5.3
THE
OBJECTIVITY
OF
NPT:
PROBLEMS
AND
MISUNDERSTANDINGS
................................
26
2.5.4
THE
OBJECTIVITY
OF
NPT:
DEFENSE
AND
UNDERSTANDING
..
27
2.5.5
RELATEDAPPROACHES
...............................
35
2.6
POPPERANDTHENEWEXPERIMENTALISTS......................
36
XII
CONTENTS
2.7
SUMMARY...............................................
38
2.8
FURTHERREADING.........................................
39
3
STATISTICS
FOR
COMPUTER
EXPERIMENTS
......................
41
3.1
HYPOTHESISTESTING
......................................
42
3.1.1
THE
TWO-SAMPLE
Z
-TEST............................
42
3.1.2
THE
TWO-SAMPLE
T
-TEST
............................
43
3.1.3
THE
PAIRED
T
-TEST
.................................
44
3.2
MONTECARLOSIMULATIONS
.................................
45
3.3
DOE:STANDARDDEFINITIONS
...............................
48
3.4
THEANALYSISOFVARIANCE.................................
48
3.5
LINEARREGRESSIONMODELS.................................
49
3.6
GRAPHICALTOOLS.........................................
51
3.6.1
HALF-NORMALPLOTS.................................
51
3.6.2
DESIGNPLOTS......................................
51
3.6.3
INTERACTIONPLOTS
..................................
51
3.6.4
BOXPLOTS
........................................
53
3.6.5
SCATTERPLOTS......................................
53
3.6.6
TRELLIS
PLOTS
......................................
54
3.7
TREE-BASEDMETHODS.....................................
55
3.8
DESIGNANDANALYSISOFCOMPUTEREXPERIMENTS..............
59
3.8.1
THESTOCHASTICPROCESSMODEL.......................
59
3.8.2
REGRESSIONMODELS.................................
59
3.8.3
CORRELATIONMODELS................................
60
3.8.4
EFFECTS
AND
INTERACTIONS
IN
THE
STOCHASTIC
PROCESS
MODEL
61
3.9
COMPARISON.............................................
62
3.10
SUMMARY...............................................
63
3.11
FURTHERREADING.........................................
64
4
OPTIMIZATION
PROBLEMS
....................................
65
4.1
PROBLEMSRELATEDTOTESTSUITES...........................
66
4.2
TESTFUNCTIONS..........................................
67
4.2.1
TEST
FUNCTION
FOR
SCHWEFEL S
SCENARIO
1
AND
2
.........
67
4.2.2
TESTFUNCTIONSFORSCHWEFEL SSCENARIO2..............
67
4.2.3
TESTFUNCTIONFORSCHWEFEL SSCENARIO3...............
69
4.3
ELEVATORGROUPCONTROL
..................................
69
4.3.1
THE
ELEVATOR
SUPERVISORY
GROUP
CONTROLLER
PROBLEM...
69
4.3.2
A
SIMPLIFIED
ELEVATOR
GROUP
CONTROL
MODEL:
THE
S-RING
72
4.3.3
THES-RINGMODELASATESTGENERATOR...............
75
4.4
RANDOMLYGENERATEDTESTPROBLEMS.......................
76
4.5
RECOMMENDATIONS.......................................
77
4.6
SUMMARY...............................................
77
4.7
FURTHERREADING.........................................
77
CONTENTS
XIII
5
DESIGNS
FOR
COMPUTER
EXPERIMENTS
........................
79
5.1
COMPUTEREXPERIMENTS...................................
80
5.2
CLASSICALALGORITHMDESIGNS
..............................
81
5.3
MODERNALGORITHMDESIGNS
...............................
84
5.4
SEQUENTIALALGORITHMDESIGNS.............................
86
5.5
PROBLEMDESIGNS
........................................
87
5.5.1
INITIALIZATION......................................
87
5.5.2
TERMINATION
......................................
89
5.6
DISCUSSION:DESIGNSFORCOMPUTEREXPERIMENTS..............
90
5.6.1
PROBLEMSRELATEDTOCLASSICALDESIGNS
...............
90
5.6.2
PROBLEMSRELATEDTOMODERNDESIGNS
................
90
5.7
RECOMMENDATIONS.......................................
90
5.8
SUMMARY...............................................
91
5.9
FURTHERREADING.........................................
92
6
SEARCH
ALGORITHMS
.........................................
93
6.1
DETERMINISTICOPTIMIZATIONALGORITHMS.....................
93
6.1.1
NELDERANDMEAD..................................
93
6.1.2
VARIABLEMETRIC
...................................
94
6.2
STOCHASTICSEARCHALGORITHMS
.............................
95
6.2.1
THETWO-MEMBEREDEVOLUTIONSTRATEGY..............
95
6.2.2
MULTIMEMBEREDEVOLUTIONSTRATEGIES.................
96
6.2.3
PARTICLESWARMOPTIMIZATION
.......................
98
6.3
SUMMARY...............................................100
6.4
FURTHERREADING.........................................101
PART
II
RESULTS
AND
PERSPECTIVES
7C
O
M
P
A
R
I
S
O
N
...............................................105
7.1
THEFICTIONOFOPTIMIZATION
..............................106
7.2
PERFORMANCEMEASURES
...................................108
7.2.1
SCENARIOS.........................................109
7.2.2
EFFECTIVITYORROBUSTNESS...........................110
7.2.3
EFFICIENCY
........................................111
7.2.4
HOW
TO
DETERMINE
THE
MAXIMUM
NUMBER
OF
ITERATIONS.118
7.3
THECLASSICALDOEAPPROACH.............................119
7.3.1
ATHREE-STAGEAPPROACH...........................119
7.3.2
TUNINGANEVOLUTIONSTRATEGY.......................120
7.4
DESIGNANDANALYSISOFCOMPUTEREXPERIMENTS..............125
7.5
SEQUENTIALPARAMETEROPTIMIZATION........................126
7.6
EXPERIMENTALRESULTS
....................................129
7.6.1
OPTIMIZING
THE
PSO
INERTIA
WEIGHT
VARIANT
..........129
7.6.2
OPTIMIZING
THE
PSO
CONSTRICTION
FACTOR
VARIANT
......135
7.6.3
COMPARINGPARTICLESWARMVARIANTS
.................138
XIV
CONTENTS
7.6.4
OPTIMIZING
THE
NELDER-MEAD
SIMPLEX
ALGORITHM
AND
AQUASI-NEWTONMETHOD
...........................138
7.7
EXPERIMENTALRESULTSFORTHES-RINGMODEL.................139
7.8
CRITERIAFORCOMPARINGALGORITHMS
........................141
7.9
SUMMARY...............................................142
7.10
FURTHERREADING.........................................143
8
UNDERSTANDING
PERFORMANCE
...............................145
8.1
SELECTIONUNDERUNCERTAINTY
..............................145
8.1.1
A
SURVEY
OF
DIFFERENT
SELECTION
SCHEMES..............146
8.1.2
INDIFFERENCEZONEAPPROACHES.......................147
8.1.3
SUBSET
SELECTION...................................148
8.1.4
THRESHOLDSELECTION................................150
8.1.5
SEQUENTIALSELECTION
...............................153
8.2
CASESTUDYI:HOWTOIMPLEMENTTHE(1+1)-ES.............153
8.2.1
THE
PROBLEM
DESIGN
SPHERE
I
.......................155
8.3
CASESTUDYII:THEEFFECTOFTHRESHOLDING..................163
8.3.1
LOCALPERFORMANCE.................................163
8.4
BOUNDED
RATIONALITY.....................................171
8.5
SUMMARY...............................................173
8.6
FURTHERREADING.........................................173
9
SUMMARY
AND
OUTLOOK
.....................................175
9.1
THENEWEXPERIMENTALISTS................................175
9.2
LEARNINGFROMERROR
.....................................176
9.3
THEORYANDEXPERIMENT..................................179
9.4
OUTLOOK................................................181
REFERENCES
.....................................................185
INDEX
..........................................................203
NOMENCLATURE
.................................................211
|
| adam_txt |
CONTENTS
PART
I
BASICS
1
RESEARCH
IN
EVOLUTIONARY
COMPUTATION
.
3
1.1
RESEARCHPROBLEMS
.
3
1.2
BACKGROUND.
4
1.2.1
EFFECTIVEAPPROACHES.
5
1.2.2
META-ALGORITHMS.
6
1.2.3
ACADEMICAPPROACHES.
6
1.2.4
APPROACHESWITHDIFFERENTGOALS
.
7
1.3
COMMON
GROUNDS:
OPTIMIZATION
RUNS
TREATED
AS
EXPERIMENTS
8
1.3.1
WIND
TUNNELS.
9
1.3.2
THENEWEXPERIMENTALISM.
10
1.4
OVERVIEWOFTHEREMAININGCHAPTERS.
10
2
THE
NEW
EXPERIMENTALISM
.
13
2.1
DEMONSTRATINGANDUNDERSTANDING.
13
2.1.1
WHY
DO
WE
NEED
EXPERIMENTS
IN
COMPUTER
SCIENCE?.
14
2.1.2
IMPORTANTRESEARCHQUESTIONS
.
17
2.2
EXPERIMENTALALGORITHMICS
.
17
2.2.1
PREEXPERIMENTALPLANNING.
17
2.2.2
GUIDELINES
FROM
EXPERIMENTAL
ALGORITHMICS.
18
2.3
OBSERVATIONALDATAANDNOISE.
19
2.4
MODELS.
20
2.5
THENEWEXPERIMENTALISM.
21
2.5.1
MAYO'SMODELSOFSTATISTICALTESTING.
23
2.5.2
NEYMAN-PEARSONPHILOSOPHY.
23
2.5.3
THE
OBJECTIVITY
OF
NPT:
PROBLEMS
AND
MISUNDERSTANDINGS
.
26
2.5.4
THE
OBJECTIVITY
OF
NPT:
DEFENSE
AND
UNDERSTANDING
.
27
2.5.5
RELATEDAPPROACHES
.
35
2.6
POPPERANDTHENEWEXPERIMENTALISTS.
36
XII
CONTENTS
2.7
SUMMARY.
38
2.8
FURTHERREADING.
39
3
STATISTICS
FOR
COMPUTER
EXPERIMENTS
.
41
3.1
HYPOTHESISTESTING
.
42
3.1.1
THE
TWO-SAMPLE
Z
-TEST.
42
3.1.2
THE
TWO-SAMPLE
T
-TEST
.
43
3.1.3
THE
PAIRED
T
-TEST
.
44
3.2
MONTECARLOSIMULATIONS
.
45
3.3
DOE:STANDARDDEFINITIONS
.
48
3.4
THEANALYSISOFVARIANCE.
48
3.5
LINEARREGRESSIONMODELS.
49
3.6
GRAPHICALTOOLS.
51
3.6.1
HALF-NORMALPLOTS.
51
3.6.2
DESIGNPLOTS.
51
3.6.3
INTERACTIONPLOTS
.
51
3.6.4
BOXPLOTS
.
53
3.6.5
SCATTERPLOTS.
53
3.6.6
TRELLIS
PLOTS
.
54
3.7
TREE-BASEDMETHODS.
55
3.8
DESIGNANDANALYSISOFCOMPUTEREXPERIMENTS.
59
3.8.1
THESTOCHASTICPROCESSMODEL.
59
3.8.2
REGRESSIONMODELS.
59
3.8.3
CORRELATIONMODELS.
60
3.8.4
EFFECTS
AND
INTERACTIONS
IN
THE
STOCHASTIC
PROCESS
MODEL
61
3.9
COMPARISON.
62
3.10
SUMMARY.
63
3.11
FURTHERREADING.
64
4
OPTIMIZATION
PROBLEMS
.
65
4.1
PROBLEMSRELATEDTOTESTSUITES.
66
4.2
TESTFUNCTIONS.
67
4.2.1
TEST
FUNCTION
FOR
SCHWEFEL'S
SCENARIO
1
AND
2
.
67
4.2.2
TESTFUNCTIONSFORSCHWEFEL'SSCENARIO2.
67
4.2.3
TESTFUNCTIONFORSCHWEFEL'SSCENARIO3.
69
4.3
ELEVATORGROUPCONTROL
.
69
4.3.1
THE
ELEVATOR
SUPERVISORY
GROUP
CONTROLLER
PROBLEM.
69
4.3.2
A
SIMPLIFIED
ELEVATOR
GROUP
CONTROL
MODEL:
THE
S-RING
72
4.3.3
THES-RINGMODELASATESTGENERATOR.
75
4.4
RANDOMLYGENERATEDTESTPROBLEMS.
76
4.5
RECOMMENDATIONS.
77
4.6
SUMMARY.
77
4.7
FURTHERREADING.
77
CONTENTS
XIII
5
DESIGNS
FOR
COMPUTER
EXPERIMENTS
.
79
5.1
COMPUTEREXPERIMENTS.
80
5.2
CLASSICALALGORITHMDESIGNS
.
81
5.3
MODERNALGORITHMDESIGNS
.
84
5.4
SEQUENTIALALGORITHMDESIGNS.
86
5.5
PROBLEMDESIGNS
.
87
5.5.1
INITIALIZATION.
87
5.5.2
TERMINATION
.
89
5.6
DISCUSSION:DESIGNSFORCOMPUTEREXPERIMENTS.
90
5.6.1
PROBLEMSRELATEDTOCLASSICALDESIGNS
.
90
5.6.2
PROBLEMSRELATEDTOMODERNDESIGNS
.
90
5.7
RECOMMENDATIONS.
90
5.8
SUMMARY.
91
5.9
FURTHERREADING.
92
6
SEARCH
ALGORITHMS
.
93
6.1
DETERMINISTICOPTIMIZATIONALGORITHMS.
93
6.1.1
NELDERANDMEAD.
93
6.1.2
VARIABLEMETRIC
.
94
6.2
STOCHASTICSEARCHALGORITHMS
.
95
6.2.1
THETWO-MEMBEREDEVOLUTIONSTRATEGY.
95
6.2.2
MULTIMEMBEREDEVOLUTIONSTRATEGIES.
96
6.2.3
PARTICLESWARMOPTIMIZATION
.
98
6.3
SUMMARY.100
6.4
FURTHERREADING.101
PART
II
RESULTS
AND
PERSPECTIVES
7C
O
M
P
A
R
I
S
O
N
.105
7.1
THEFICTIONOFOPTIMIZATION
.106
7.2
PERFORMANCEMEASURES
.108
7.2.1
SCENARIOS.109
7.2.2
EFFECTIVITYORROBUSTNESS.110
7.2.3
EFFICIENCY
.111
7.2.4
HOW
TO
DETERMINE
THE
MAXIMUM
NUMBER
OF
ITERATIONS.118
7.3
THECLASSICALDOEAPPROACH.119
7.3.1
ATHREE-STAGEAPPROACH.119
7.3.2
TUNINGANEVOLUTIONSTRATEGY.120
7.4
DESIGNANDANALYSISOFCOMPUTEREXPERIMENTS.125
7.5
SEQUENTIALPARAMETEROPTIMIZATION.126
7.6
EXPERIMENTALRESULTS
.129
7.6.1
OPTIMIZING
THE
PSO
INERTIA
WEIGHT
VARIANT
.129
7.6.2
OPTIMIZING
THE
PSO
CONSTRICTION
FACTOR
VARIANT
.135
7.6.3
COMPARINGPARTICLESWARMVARIANTS
.138
XIV
CONTENTS
7.6.4
OPTIMIZING
THE
NELDER-MEAD
SIMPLEX
ALGORITHM
AND
AQUASI-NEWTONMETHOD
.138
7.7
EXPERIMENTALRESULTSFORTHES-RINGMODEL.139
7.8
CRITERIAFORCOMPARINGALGORITHMS
.141
7.9
SUMMARY.142
7.10
FURTHERREADING.143
8
UNDERSTANDING
PERFORMANCE
.145
8.1
SELECTIONUNDERUNCERTAINTY
.145
8.1.1
A
SURVEY
OF
DIFFERENT
SELECTION
SCHEMES.146
8.1.2
INDIFFERENCEZONEAPPROACHES.147
8.1.3
SUBSET
SELECTION.148
8.1.4
THRESHOLDSELECTION.150
8.1.5
SEQUENTIALSELECTION
.153
8.2
CASESTUDYI:HOWTOIMPLEMENTTHE(1+1)-ES.153
8.2.1
THE
PROBLEM
DESIGN
SPHERE
I
.155
8.3
CASESTUDYII:THEEFFECTOFTHRESHOLDING.163
8.3.1
LOCALPERFORMANCE.163
8.4
BOUNDED
RATIONALITY.171
8.5
SUMMARY.173
8.6
FURTHERREADING.173
9
SUMMARY
AND
OUTLOOK
.175
9.1
THENEWEXPERIMENTALISTS.175
9.2
LEARNINGFROMERROR
.176
9.3
THEORYANDEXPERIMENT.179
9.4
OUTLOOK.181
REFERENCES
.185
INDEX
.203
NOMENCLATURE
.211 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Bartz-Beielstein, Thomas 1966- |
| author_GND | (DE-588)124999476 |
| author_facet | Bartz-Beielstein, Thomas 1966- |
| author_role | aut |
| author_sort | Bartz-Beielstein, Thomas 1966- |
| author_variant | t b b tbb |
| building | Verbundindex |
| bvnumber | BV021686692 |
| classification_rvk | ST 134 |
| ctrlnum | (OCoLC)181555471 (DE-599)BVBBV021686692 |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| format | Book |
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| id | DE-604.BV021686692 |
| illustrated | Illustrated |
| index_date | 2024-07-02T15:13:09Z |
| indexdate | 2024-07-09T20:41:39Z |
| institution | BVB |
| isbn | 9783540320265 3540320261 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014900811 |
| oclc_num | 181555471 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | XIV, 214 S. graph. Darst. 24 cm |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| record_format | marc |
| series2 | Natural computing series |
| spelling | Bartz-Beielstein, Thomas 1966- Verfasser (DE-588)124999476 aut Experimental research in evolutionary computation the new experimentalism ; with 36 tables Thomas Bartz-Beielstein Berlin [u.a.] Springer 2006 XIV, 214 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Natural computing series Literaturverz. S. 185 - 202 Evolutionary computation Research Evolutionary programming (Computer science) Research Research Methodology Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Versuchsplanung (DE-588)4078859-3 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 s Versuchsplanung (DE-588)4078859-3 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014900811&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bartz-Beielstein, Thomas 1966- Experimental research in evolutionary computation the new experimentalism ; with 36 tables Evolutionary computation Research Evolutionary programming (Computer science) Research Research Methodology Evolutionärer Algorithmus (DE-588)4366912-8 gnd Versuchsplanung (DE-588)4078859-3 gnd |
| subject_GND | (DE-588)4366912-8 (DE-588)4078859-3 |
| title | Experimental research in evolutionary computation the new experimentalism ; with 36 tables |
| title_auth | Experimental research in evolutionary computation the new experimentalism ; with 36 tables |
| title_exact_search | Experimental research in evolutionary computation the new experimentalism ; with 36 tables |
| title_exact_search_txtP | Experimental research in evolutionary computation the new experimentalism ; with 36 tables |
| title_full | Experimental research in evolutionary computation the new experimentalism ; with 36 tables Thomas Bartz-Beielstein |
| title_fullStr | Experimental research in evolutionary computation the new experimentalism ; with 36 tables Thomas Bartz-Beielstein |
| title_full_unstemmed | Experimental research in evolutionary computation the new experimentalism ; with 36 tables Thomas Bartz-Beielstein |
| title_short | Experimental research in evolutionary computation |
| title_sort | experimental research in evolutionary computation the new experimentalism with 36 tables |
| title_sub | the new experimentalism ; with 36 tables |
| topic | Evolutionary computation Research Evolutionary programming (Computer science) Research Research Methodology Evolutionärer Algorithmus (DE-588)4366912-8 gnd Versuchsplanung (DE-588)4078859-3 gnd |
| topic_facet | Evolutionary computation Research Evolutionary programming (Computer science) Research Research Methodology Evolutionärer Algorithmus Versuchsplanung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014900811&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bartzbeielsteinthomas experimentalresearchinevolutionarycomputationthenewexperimentalismwith36tables |