Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set
Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC’05 benchmark set for continuous function optimization. In particular, we consider a sepa...
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Gespeichert in:
| Autor*in: |
Liao, Tianjun [verfasserIn] de Oca, Marco A. Montes [verfasserIn] Stützle, Thomas [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 17(2012), 6 vom: 11. Dez., Seite 1031-1046 |
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| Übergeordnetes Werk: |
volume:17 ; year:2012 ; number:6 ; day:11 ; month:12 ; pages:1031-1046 |
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| DOI / URN: |
10.1007/s00500-012-0946-x |
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| 520 | |a Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC’05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC’05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search. | ||
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10.1007/s00500-012-0946-x doi (DE-627)SPR006482554 (SPR)s00500-012-0946-x-e DE-627 ger DE-627 rakwb eng Liao, Tianjun verfasserin aut Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC’05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC’05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search. Automatic algorithm configuration (dpeaa)DE-He213 CMA-ES (dpeaa)DE-He213 Continuous optimization (dpeaa)DE-He213 de Oca, Marco A. Montes verfasserin aut Stützle, Thomas verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 17(2012), 6 vom: 11. Dez., Seite 1031-1046 (DE-627)SPR006469531 nnns volume:17 year:2012 number:6 day:11 month:12 pages:1031-1046 https://dx.doi.org/10.1007/s00500-012-0946-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 17 2012 6 11 12 1031-1046 |
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10.1007/s00500-012-0946-x doi (DE-627)SPR006482554 (SPR)s00500-012-0946-x-e DE-627 ger DE-627 rakwb eng Liao, Tianjun verfasserin aut Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC’05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC’05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search. Automatic algorithm configuration (dpeaa)DE-He213 CMA-ES (dpeaa)DE-He213 Continuous optimization (dpeaa)DE-He213 de Oca, Marco A. Montes verfasserin aut Stützle, Thomas verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 17(2012), 6 vom: 11. Dez., Seite 1031-1046 (DE-627)SPR006469531 nnns volume:17 year:2012 number:6 day:11 month:12 pages:1031-1046 https://dx.doi.org/10.1007/s00500-012-0946-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 17 2012 6 11 12 1031-1046 |
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10.1007/s00500-012-0946-x doi (DE-627)SPR006482554 (SPR)s00500-012-0946-x-e DE-627 ger DE-627 rakwb eng Liao, Tianjun verfasserin aut Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC’05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC’05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search. Automatic algorithm configuration (dpeaa)DE-He213 CMA-ES (dpeaa)DE-He213 Continuous optimization (dpeaa)DE-He213 de Oca, Marco A. Montes verfasserin aut Stützle, Thomas verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 17(2012), 6 vom: 11. Dez., Seite 1031-1046 (DE-627)SPR006469531 nnns volume:17 year:2012 number:6 day:11 month:12 pages:1031-1046 https://dx.doi.org/10.1007/s00500-012-0946-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 17 2012 6 11 12 1031-1046 |
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10.1007/s00500-012-0946-x doi (DE-627)SPR006482554 (SPR)s00500-012-0946-x-e DE-627 ger DE-627 rakwb eng Liao, Tianjun verfasserin aut Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC’05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC’05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search. Automatic algorithm configuration (dpeaa)DE-He213 CMA-ES (dpeaa)DE-He213 Continuous optimization (dpeaa)DE-He213 de Oca, Marco A. Montes verfasserin aut Stützle, Thomas verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 17(2012), 6 vom: 11. Dez., Seite 1031-1046 (DE-627)SPR006469531 nnns volume:17 year:2012 number:6 day:11 month:12 pages:1031-1046 https://dx.doi.org/10.1007/s00500-012-0946-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 17 2012 6 11 12 1031-1046 |
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10.1007/s00500-012-0946-x doi (DE-627)SPR006482554 (SPR)s00500-012-0946-x-e DE-627 ger DE-627 rakwb eng Liao, Tianjun verfasserin aut Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC’05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC’05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search. Automatic algorithm configuration (dpeaa)DE-He213 CMA-ES (dpeaa)DE-He213 Continuous optimization (dpeaa)DE-He213 de Oca, Marco A. Montes verfasserin aut Stützle, Thomas verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 17(2012), 6 vom: 11. Dez., Seite 1031-1046 (DE-627)SPR006469531 nnns volume:17 year:2012 number:6 day:11 month:12 pages:1031-1046 https://dx.doi.org/10.1007/s00500-012-0946-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 17 2012 6 11 12 1031-1046 |
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Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC’05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC’05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search. |
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Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC’05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC’05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search. |
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Abstract In this article, we apply an automatic algorithm configuration tool to improve the performance of the CMA-ES algorithm with increasing population size (iCMA-ES), the best performing algorithm on the CEC’05 benchmark set for continuous function optimization. In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC’05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search. |
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In particular, we consider a separation between tuning and test sets and, thus, tune iCMA-ES on a different set of functions than the ones of the CEC’05 benchmark set. Our experimental results show that the tuned iCMA-ES improves significantly over the default version of iCMA-ES. Furthermore, we provide some further analyses on the impact of the modified parameter settings on iCMA-ES performance and a comparison with recent results of algorithms that use CMA-ES as a subordinate local search.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Automatic algorithm configuration</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CMA-ES</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Continuous optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">de Oca, Marco A. Montes</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Stützle, Thomas</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">17(2012), 6 vom: 11. Dez., Seite 1031-1046</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:17</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:6</subfield><subfield code="g">day:11</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:1031-1046</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-012-0946-x</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">17</subfield><subfield code="j">2012</subfield><subfield code="e">6</subfield><subfield code="b">11</subfield><subfield code="c">12</subfield><subfield code="h">1031-1046</subfield></datafield></record></collection>
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