Dataset Open Access

An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization

Sibghat Ullah; Hao Wang; Stefan Menzel; Thomas Bäck; Bernhard Sendhoff


DCAT Export

<?xml version='1.0' encoding='utf-8'?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#">
  <rdf:Description rdf:about="https://doi.org/10.5281/zenodo.3854910">
    <rdf:type rdf:resource="http://www.w3.org/ns/dcat#Dataset"/>
    <dct:type rdf:resource="http://purl.org/dc/dcmitype/Dataset"/>
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://doi.org/10.5281/zenodo.3854910</dct:identifier>
    <foaf:page rdf:resource="https://doi.org/10.5281/zenodo.3854910"/>
    <dct:creator>
      <rdf:Description rdf:about="http://orcid.org/0000-0002-2627-6019">
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">0000-0002-2627-6019</dct:identifier>
        <foaf:name>Sibghat Ullah</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>University of Leiden</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description rdf:about="http://orcid.org/0000-0002-4933-5181">
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">0000-0002-4933-5181</dct:identifier>
        <foaf:name>Hao Wang</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>University of Leiden</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Stefan Menzel</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>Honda Research Institute Europe GmBH</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description rdf:about="http://orcid.org/0000-0001-6768-1478">
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">0000-0001-6768-1478</dct:identifier>
        <foaf:name>Thomas Bäck</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>University of Leiden</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description rdf:about="http://orcid.org/0000-0002-1233-9584">
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">0000-0002-1233-9584</dct:identifier>
        <foaf:name>Bernhard Sendhoff</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>Honda Research Institute Europe GmBH</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:title>An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization</dct:title>
    <dct:publisher>
      <foaf:Agent>
        <foaf:name>Zenodo</foaf:name>
      </foaf:Agent>
    </dct:publisher>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
    <dcat:keyword>meta-modeling</dcat:keyword>
    <dcat:keyword>surrogate-assisted optimization</dcat:keyword>
    <dcat:keyword>robust optimization</dcat:keyword>
    <dcat:keyword>quality engineering</dcat:keyword>
    <dcat:keyword>machine learning</dcat:keyword>
    <frapo:isFundedBy rdf:resource="info:eu-repo/grantAgreement/EC/H2020/766186/"/>
    <schema:funder>
      <foaf:Organization>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100000780</dct:identifier>
        <foaf:name>European Commission</foaf:name>
      </foaf:Organization>
    </schema:funder>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-02-20</dct:issued>
    <dct:language rdf:resource="http://publications.europa.eu/resource/authority/language/ENG"/>
    <owl:sameAs rdf:resource="https://zenodo.org/record/3854910"/>
    <adms:identifier>
      <adms:Identifier>
        <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/3854910</skos:notation>
        <adms:schemeAgency>url</adms:schemeAgency>
      </adms:Identifier>
    </adms:identifier>
    <dct:isVersionOf rdf:resource="https://doi.org/10.5281/zenodo.3854909"/>
    <dct:isPartOf rdf:resource="https://zenodo.org/communities/ecole_itn"/>
    <owl:versionInfo>1</owl:versionInfo>
    <dct:description>&lt;p&gt;This is the data and source code used in the paper below:&lt;/p&gt; &lt;p&gt;Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas B&amp;auml;ck, &amp;ldquo;An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization&amp;rdquo;, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi:&amp;nbsp;10.1109/SSCI44817.2019.9002805&lt;/p&gt; &lt;p&gt;This research investigates the potential of using meta-modeling techniques in the context of robust optimization namely optimization under uncertainty/noise. A systematic empirical comparison is performed for evaluating and comparing different meta-modeling techniques for robust optimization. The experimental setup includes three noise levels, six meta-modeling algorithms, and six benchmark problems from the continuous optimization domain, each for three different dimensionalities. Two robustness definitions: robust regularization and robust composition, are used in the experiments. The meta-modeling techniques are evaluated and compared with respect to the modeling accuracy and the optimal function values. The results clearly show that Kriging, Support Vector Machine and Polynomial regression perform excellently as they achieve high accuracy and the optimal point on the model landscape is close to the true optimum of test functions in most cases.&lt;/p&gt;</dct:description>
    <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/>
    <dct:accessRights>
      <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess">
        <rdfs:label>Open Access</rdfs:label>
      </dct:RightsStatement>
    </dct:accessRights>
    <dcat:distribution>
      <dcat:Distribution>
        <dct:license rdf:resource="https://creativecommons.org/licenses/by-sa/4.0/legalcode"/>
        <dcat:accessURL rdf:resource="https://doi.org/10.5281/zenodo.3854910"/>
      </dcat:Distribution>
    </dcat:distribution>
    <dcat:distribution>
      <dcat:Distribution>
        <dcat:accessURL>https://doi.org/10.5281/zenodo.3854910</dcat:accessURL>
        <dcat:byteSize>1621762</dcat:byteSize>
        <dcat:downloadURL>https://zenodo.org/record/3854910/files/MetaModelComparison-ContinuousBlackBoxFunction-Code-190601-Open.zip</dcat:downloadURL>
        <dcat:mediaType>application/zip</dcat:mediaType>
      </dcat:Distribution>
    </dcat:distribution>
    <dcat:distribution>
      <dcat:Distribution>
        <dcat:accessURL>https://doi.org/10.5281/zenodo.3854910</dcat:accessURL>
        <dcat:byteSize>4030574</dcat:byteSize>
        <dcat:downloadURL>https://zenodo.org/record/3854910/files/MetaModelComparison-ContinuousBlackBoxFunction-Input-190301-Open.zip</dcat:downloadURL>
        <dcat:mediaType>application/zip</dcat:mediaType>
      </dcat:Distribution>
    </dcat:distribution>
    <dcat:distribution>
      <dcat:Distribution>
        <dcat:accessURL>https://doi.org/10.5281/zenodo.3854910</dcat:accessURL>
        <dcat:byteSize>402520</dcat:byteSize>
        <dcat:downloadURL>https://zenodo.org/record/3854910/files/MetaModelComparison-ContinuousBlackBoxFunction-Output-190401-Open.zip</dcat:downloadURL>
        <dcat:mediaType>application/zip</dcat:mediaType>
      </dcat:Distribution>
    </dcat:distribution>
  </rdf:Description>
  <foaf:Project rdf:about="info:eu-repo/grantAgreement/EC/H2020/766186/">
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">766186</dct:identifier>
    <dct:title>Experience-based Computation: Learning to Optimise</dct:title>
    <frapo:isAwardedBy>
      <foaf:Organization>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100000780</dct:identifier>
        <foaf:name>European Commission</foaf:name>
      </foaf:Organization>
    </frapo:isAwardedBy>
  </foaf:Project>
</rdf:RDF>
20
1
views
downloads
All versions This version
Views 2020
Downloads 11
Data volume 1.6 MB1.6 MB
Unique views 1414
Unique downloads 11

Share

Cite as