Comparison of metamodeling techniques in evolutionary algorithms
Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of t...
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Gespeichert in:
| Autor*in: |
Díaz-Manríquez, Alan [verfasserIn] Toscano, Gregorio [verfasserIn] Coello Coello, Carlos A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 21(2016), 19 vom: 19. Apr., Seite 5647-5663 |
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| Übergeordnetes Werk: |
volume:21 ; year:2016 ; number:19 ; day:19 ; month:04 ; pages:5647-5663 |
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| DOI / URN: |
10.1007/s00500-016-2140-z |
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SPR006493688 |
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| 520 | |a Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation. | ||
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10.1007/s00500-016-2140-z doi (DE-627)SPR006493688 (SPR)s00500-016-2140-z-e DE-627 ger DE-627 rakwb eng Díaz-Manríquez, Alan verfasserin aut Comparison of metamodeling techniques in evolutionary algorithms 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation. Surrogate models (dpeaa)DE-He213 Evolutionary algorithms (dpeaa)DE-He213 Local models (dpeaa)DE-He213 Toscano, Gregorio verfasserin aut Coello Coello, Carlos A. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 19 vom: 19. Apr., Seite 5647-5663 (DE-627)SPR006469531 nnns volume:21 year:2016 number:19 day:19 month:04 pages:5647-5663 https://dx.doi.org/10.1007/s00500-016-2140-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 19 19 04 5647-5663 |
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10.1007/s00500-016-2140-z doi (DE-627)SPR006493688 (SPR)s00500-016-2140-z-e DE-627 ger DE-627 rakwb eng Díaz-Manríquez, Alan verfasserin aut Comparison of metamodeling techniques in evolutionary algorithms 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation. Surrogate models (dpeaa)DE-He213 Evolutionary algorithms (dpeaa)DE-He213 Local models (dpeaa)DE-He213 Toscano, Gregorio verfasserin aut Coello Coello, Carlos A. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 19 vom: 19. Apr., Seite 5647-5663 (DE-627)SPR006469531 nnns volume:21 year:2016 number:19 day:19 month:04 pages:5647-5663 https://dx.doi.org/10.1007/s00500-016-2140-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 19 19 04 5647-5663 |
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10.1007/s00500-016-2140-z doi (DE-627)SPR006493688 (SPR)s00500-016-2140-z-e DE-627 ger DE-627 rakwb eng Díaz-Manríquez, Alan verfasserin aut Comparison of metamodeling techniques in evolutionary algorithms 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation. Surrogate models (dpeaa)DE-He213 Evolutionary algorithms (dpeaa)DE-He213 Local models (dpeaa)DE-He213 Toscano, Gregorio verfasserin aut Coello Coello, Carlos A. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 19 vom: 19. Apr., Seite 5647-5663 (DE-627)SPR006469531 nnns volume:21 year:2016 number:19 day:19 month:04 pages:5647-5663 https://dx.doi.org/10.1007/s00500-016-2140-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 19 19 04 5647-5663 |
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10.1007/s00500-016-2140-z doi (DE-627)SPR006493688 (SPR)s00500-016-2140-z-e DE-627 ger DE-627 rakwb eng Díaz-Manríquez, Alan verfasserin aut Comparison of metamodeling techniques in evolutionary algorithms 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation. Surrogate models (dpeaa)DE-He213 Evolutionary algorithms (dpeaa)DE-He213 Local models (dpeaa)DE-He213 Toscano, Gregorio verfasserin aut Coello Coello, Carlos A. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 19 vom: 19. Apr., Seite 5647-5663 (DE-627)SPR006469531 nnns volume:21 year:2016 number:19 day:19 month:04 pages:5647-5663 https://dx.doi.org/10.1007/s00500-016-2140-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 19 19 04 5647-5663 |
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10.1007/s00500-016-2140-z doi (DE-627)SPR006493688 (SPR)s00500-016-2140-z-e DE-627 ger DE-627 rakwb eng Díaz-Manríquez, Alan verfasserin aut Comparison of metamodeling techniques in evolutionary algorithms 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation. Surrogate models (dpeaa)DE-He213 Evolutionary algorithms (dpeaa)DE-He213 Local models (dpeaa)DE-He213 Toscano, Gregorio verfasserin aut Coello Coello, Carlos A. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 19 vom: 19. Apr., Seite 5647-5663 (DE-627)SPR006469531 nnns volume:21 year:2016 number:19 day:19 month:04 pages:5647-5663 https://dx.doi.org/10.1007/s00500-016-2140-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 19 19 04 5647-5663 |
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Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation. |
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Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation. |
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Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006493688</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002829.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-016-2140-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006493688</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-016-2140-z-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Díaz-Manríquez, Alan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Comparison of metamodeling techniques in evolutionary algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Surrogate models</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Evolutionary algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Local models</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Toscano, Gregorio</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Coello Coello, Carlos A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">21(2016), 19 vom: 19. Apr., Seite 5647-5663</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:21</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:19</subfield><subfield code="g">day:19</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:5647-5663</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-016-2140-z</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">21</subfield><subfield code="j">2016</subfield><subfield code="e">19</subfield><subfield code="b">19</subfield><subfield code="c">04</subfield><subfield code="h">5647-5663</subfield></datafield></record></collection>
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