Multiobjective Optimization on a Budget of 250 Evaluations View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2005

AUTHORS

Joshua Knowles , Evan J. Hughes

ABSTRACT

In engineering and other ‘real-world’ applications, multiobjective optimization problems must frequently be tackled on a tight evaluation budget — tens or hundreds of function evaluations, rather than thousands. In this paper, we investigate two algorithms that use advanced initialization and search strategies to operate better under these conditions. The first algorithm, Bin_MSOPS, uses a binary search tree to divide up the decision space, and tries to sample from the largest empty regions near ‘fit’ solutions. The second algorithm, ParEGO, begins with solutions in a latin hypercube and updates a Gaussian processes surrogate model of the search landscape after every function evaluation, which it uses to estimate the solution of largest expected improvement. The two algorithms are tested using a benchmark suite of nine functions of two and three objectives — on a budget of only 250 function evaluations each, in total. Results indicate that the two algorithms search the space in very different ways and this can be used to understand performance differences. Both algorithms perform well but ParEGO comes out on top in seven of the nine test cases after 100 function evaluations, and on six after the first 250 evaluations. More... »

PAGES

176-190

Book

TITLE

Evolutionary Multi-Criterion Optimization

ISBN

978-3-540-24983-2
978-3-540-31880-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-31880-4_13

DOI

http://dx.doi.org/10.1007/978-3-540-31880-4_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1006831024


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Applied Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Manchester", 
          "id": "https://www.grid.ac/institutes/grid.5379.8", 
          "name": [
            "School of Chemistry, University of Manchester, PO Box 88, Faraday Building, Sackville Street, M60 1QD, Manchester, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Knowles", 
        "givenName": "Joshua", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cranfield University", 
          "id": "https://www.grid.ac/institutes/grid.12026.37", 
          "name": [
            "Cranfield University, SN6 8LA, Shrivenham, Swindon, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hughes", 
        "givenName": "Evan J.", 
        "id": "sg:person.013547430723.21", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013547430723.21"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-3-540-30217-9_80", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000957882", 
          "https://doi.org/10.1007/978-3-540-30217-9_80"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-30217-9_80", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000957882", 
          "https://doi.org/10.1007/978-3-540-30217-9_80"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-36970-8_3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005119556", 
          "https://doi.org/10.1007/3-540-36970-8_3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1008306431147", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009040383", 
          "https://doi.org/10.1023/a:1008306431147"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-45712-7_29", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010472546", 
          "https://doi.org/10.1007/3-540-45712-7_29"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-45712-7_29", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010472546", 
          "https://doi.org/10.1007/3-540-45712-7_29"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1162/106365601750190406", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014679884"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-36970-8_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017034348", 
          "https://doi.org/10.1007/3-540-36970-8_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02591870", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019166567", 
          "https://doi.org/10.1007/bf02591870"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02591870", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019166567", 
          "https://doi.org/10.1007/bf02591870"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0955-2219(01)00289-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023205308"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-61723-x_1022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024732816", 
          "https://doi.org/10.1007/3-540-61723-x_1022"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/1-84628-137-7_6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028475054", 
          "https://doi.org/10.1007/1-84628-137-7_6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/298151.298382", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036821240"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-45712-7_12", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037946614", 
          "https://doi.org/10.1007/3-540-45712-7_12"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-45712-7_12", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037946614", 
          "https://doi.org/10.1007/3-540-45712-7_12"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0032-9592(94)00036-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048758757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac034669a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054995215"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac034669a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054995215"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tevc.2002.800884", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061604548"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tevc.2003.810758", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061604587"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tevc.2005.851274", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061604686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/ss/1177012413", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064409909"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cec.2003.1299427", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095471252"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2005", 
    "datePublishedReg": "2005-01-01", 
    "description": "In engineering and other \u2018real-world\u2019 applications, multiobjective optimization problems must frequently be tackled on a tight evaluation budget \u2014 tens or hundreds of function evaluations, rather than thousands. In this paper, we investigate two algorithms that use advanced initialization and search strategies to operate better under these conditions. The first algorithm, Bin_MSOPS, uses a binary search tree to divide up the decision space, and tries to sample from the largest empty regions near \u2018fit\u2019 solutions. The second algorithm, ParEGO, begins with solutions in a latin hypercube and updates a Gaussian processes surrogate model of the search landscape after every function evaluation, which it uses to estimate the solution of largest expected improvement. The two algorithms are tested using a benchmark suite of nine functions of two and three objectives \u2014 on a budget of only 250 function evaluations each, in total. Results indicate that the two algorithms search the space in very different ways and this can be used to understand performance differences. Both algorithms perform well but ParEGO comes out on top in seven of the nine test cases after 100 function evaluations, and on six after the first 250 evaluations.", 
    "editor": [
      {
        "familyName": "Coello Coello", 
        "givenName": "Carlos A.", 
        "type": "Person"
      }, 
      {
        "familyName": "Hern\u00e1ndez Aguirre", 
        "givenName": "Arturo", 
        "type": "Person"
      }, 
      {
        "familyName": "Zitzler", 
        "givenName": "Eckart", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-31880-4_13", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-24983-2", 
        "978-3-540-31880-4"
      ], 
      "name": "Evolutionary Multi-Criterion Optimization", 
      "type": "Book"
    }, 
    "name": "Multiobjective Optimization on a Budget of 250 Evaluations", 
    "pagination": "176-190", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1006831024"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-31880-4_13"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "208a022c68629486f0c1e1e69c4a4ab6a580823168120fd37939ff7dbf42b00c"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-31880-4_13", 
      "https://app.dimensions.ai/details/publication/pub.1006831024"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T08:03", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000359_0000000359/records_29204_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-540-31880-4_13"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-31880-4_13'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-31880-4_13'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-31880-4_13'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-31880-4_13'


 

This table displays all metadata directly associated to this object as RDF triples.

150 TRIPLES      23 PREDICATES      46 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-31880-4_13 schema:about anzsrc-for:01
2 anzsrc-for:0102
3 schema:author N8563b30a058a40039adc142569fb4e5d
4 schema:citation sg:pub.10.1007/1-84628-137-7_6
5 sg:pub.10.1007/3-540-36970-8_3
6 sg:pub.10.1007/3-540-36970-8_8
7 sg:pub.10.1007/3-540-45712-7_12
8 sg:pub.10.1007/3-540-45712-7_29
9 sg:pub.10.1007/3-540-61723-x_1022
10 sg:pub.10.1007/978-3-540-30217-9_80
11 sg:pub.10.1007/bf02591870
12 sg:pub.10.1023/a:1008306431147
13 https://doi.org/10.1016/0032-9592(94)00036-0
14 https://doi.org/10.1016/s0955-2219(01)00289-8
15 https://doi.org/10.1021/ac034669a
16 https://doi.org/10.1109/cec.2003.1299427
17 https://doi.org/10.1109/tevc.2002.800884
18 https://doi.org/10.1109/tevc.2003.810758
19 https://doi.org/10.1109/tevc.2005.851274
20 https://doi.org/10.1145/298151.298382
21 https://doi.org/10.1162/106365601750190406
22 https://doi.org/10.1214/ss/1177012413
23 schema:datePublished 2005
24 schema:datePublishedReg 2005-01-01
25 schema:description In engineering and other ‘real-world’ applications, multiobjective optimization problems must frequently be tackled on a tight evaluation budget — tens or hundreds of function evaluations, rather than thousands. In this paper, we investigate two algorithms that use advanced initialization and search strategies to operate better under these conditions. The first algorithm, Bin_MSOPS, uses a binary search tree to divide up the decision space, and tries to sample from the largest empty regions near ‘fit’ solutions. The second algorithm, ParEGO, begins with solutions in a latin hypercube and updates a Gaussian processes surrogate model of the search landscape after every function evaluation, which it uses to estimate the solution of largest expected improvement. The two algorithms are tested using a benchmark suite of nine functions of two and three objectives — on a budget of only 250 function evaluations each, in total. Results indicate that the two algorithms search the space in very different ways and this can be used to understand performance differences. Both algorithms perform well but ParEGO comes out on top in seven of the nine test cases after 100 function evaluations, and on six after the first 250 evaluations.
26 schema:editor N6d399ea1e945495f8f4edb428f921f8e
27 schema:genre chapter
28 schema:inLanguage en
29 schema:isAccessibleForFree true
30 schema:isPartOf Nf358f7b8179945fc8dbdfec1f78b1416
31 schema:name Multiobjective Optimization on a Budget of 250 Evaluations
32 schema:pagination 176-190
33 schema:productId N6aec7c9c50024cfe805ba45d6b105929
34 Nee65263a182444b190109d678bd2fd42
35 Nfd220bdfd5674a92bb4bd34b1af86ee4
36 schema:publisher N60c37a9fd3004015bd67844500c3549e
37 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006831024
38 https://doi.org/10.1007/978-3-540-31880-4_13
39 schema:sdDatePublished 2019-04-16T08:03
40 schema:sdLicense https://scigraph.springernature.com/explorer/license/
41 schema:sdPublisher N1043765c355e4793a7f2bdbe3d38280d
42 schema:url https://link.springer.com/10.1007%2F978-3-540-31880-4_13
43 sgo:license sg:explorer/license/
44 sgo:sdDataset chapters
45 rdf:type schema:Chapter
46 N1043765c355e4793a7f2bdbe3d38280d schema:name Springer Nature - SN SciGraph project
47 rdf:type schema:Organization
48 N1f2a200a6c6d4107a45b5b0e9aceeb67 rdf:first sg:person.013547430723.21
49 rdf:rest rdf:nil
50 N2a85c37a879d4b6e93deae8bee3ab4d7 rdf:first N67a791de6cfa49298ea0a294664e2263
51 rdf:rest rdf:nil
52 N353d3ee6ab96462eb6dbd9517098ef99 schema:familyName Hernández Aguirre
53 schema:givenName Arturo
54 rdf:type schema:Person
55 N60c37a9fd3004015bd67844500c3549e schema:location Berlin, Heidelberg
56 schema:name Springer Berlin Heidelberg
57 rdf:type schema:Organisation
58 N67a791de6cfa49298ea0a294664e2263 schema:familyName Zitzler
59 schema:givenName Eckart
60 rdf:type schema:Person
61 N6aec7c9c50024cfe805ba45d6b105929 schema:name dimensions_id
62 schema:value pub.1006831024
63 rdf:type schema:PropertyValue
64 N6d399ea1e945495f8f4edb428f921f8e rdf:first Na2a9d522ba5d4ff6813b81b591bf9004
65 rdf:rest Nbf2cccc6f7f04e9d94547eba37286418
66 N8563b30a058a40039adc142569fb4e5d rdf:first Na339ab319dec49858fd2fbba5997035e
67 rdf:rest N1f2a200a6c6d4107a45b5b0e9aceeb67
68 Na2a9d522ba5d4ff6813b81b591bf9004 schema:familyName Coello Coello
69 schema:givenName Carlos A.
70 rdf:type schema:Person
71 Na339ab319dec49858fd2fbba5997035e schema:affiliation https://www.grid.ac/institutes/grid.5379.8
72 schema:familyName Knowles
73 schema:givenName Joshua
74 rdf:type schema:Person
75 Nbf2cccc6f7f04e9d94547eba37286418 rdf:first N353d3ee6ab96462eb6dbd9517098ef99
76 rdf:rest N2a85c37a879d4b6e93deae8bee3ab4d7
77 Nee65263a182444b190109d678bd2fd42 schema:name readcube_id
78 schema:value 208a022c68629486f0c1e1e69c4a4ab6a580823168120fd37939ff7dbf42b00c
79 rdf:type schema:PropertyValue
80 Nf358f7b8179945fc8dbdfec1f78b1416 schema:isbn 978-3-540-24983-2
81 978-3-540-31880-4
82 schema:name Evolutionary Multi-Criterion Optimization
83 rdf:type schema:Book
84 Nfd220bdfd5674a92bb4bd34b1af86ee4 schema:name doi
85 schema:value 10.1007/978-3-540-31880-4_13
86 rdf:type schema:PropertyValue
87 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
88 schema:name Mathematical Sciences
89 rdf:type schema:DefinedTerm
90 anzsrc-for:0102 schema:inDefinedTermSet anzsrc-for:
91 schema:name Applied Mathematics
92 rdf:type schema:DefinedTerm
93 sg:person.013547430723.21 schema:affiliation https://www.grid.ac/institutes/grid.12026.37
94 schema:familyName Hughes
95 schema:givenName Evan J.
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013547430723.21
97 rdf:type schema:Person
98 sg:pub.10.1007/1-84628-137-7_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028475054
99 https://doi.org/10.1007/1-84628-137-7_6
100 rdf:type schema:CreativeWork
101 sg:pub.10.1007/3-540-36970-8_3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005119556
102 https://doi.org/10.1007/3-540-36970-8_3
103 rdf:type schema:CreativeWork
104 sg:pub.10.1007/3-540-36970-8_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017034348
105 https://doi.org/10.1007/3-540-36970-8_8
106 rdf:type schema:CreativeWork
107 sg:pub.10.1007/3-540-45712-7_12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037946614
108 https://doi.org/10.1007/3-540-45712-7_12
109 rdf:type schema:CreativeWork
110 sg:pub.10.1007/3-540-45712-7_29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010472546
111 https://doi.org/10.1007/3-540-45712-7_29
112 rdf:type schema:CreativeWork
113 sg:pub.10.1007/3-540-61723-x_1022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024732816
114 https://doi.org/10.1007/3-540-61723-x_1022
115 rdf:type schema:CreativeWork
116 sg:pub.10.1007/978-3-540-30217-9_80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000957882
117 https://doi.org/10.1007/978-3-540-30217-9_80
118 rdf:type schema:CreativeWork
119 sg:pub.10.1007/bf02591870 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019166567
120 https://doi.org/10.1007/bf02591870
121 rdf:type schema:CreativeWork
122 sg:pub.10.1023/a:1008306431147 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009040383
123 https://doi.org/10.1023/a:1008306431147
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/0032-9592(94)00036-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048758757
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/s0955-2219(01)00289-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023205308
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1021/ac034669a schema:sameAs https://app.dimensions.ai/details/publication/pub.1054995215
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1109/cec.2003.1299427 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095471252
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1109/tevc.2002.800884 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061604548
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1109/tevc.2003.810758 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061604587
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1109/tevc.2005.851274 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061604686
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1145/298151.298382 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036821240
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1162/106365601750190406 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014679884
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1214/ss/1177012413 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064409909
144 rdf:type schema:CreativeWork
145 https://www.grid.ac/institutes/grid.12026.37 schema:alternateName Cranfield University
146 schema:name Cranfield University, SN6 8LA, Shrivenham, Swindon, UK
147 rdf:type schema:Organization
148 https://www.grid.ac/institutes/grid.5379.8 schema:alternateName University of Manchester
149 schema:name School of Chemistry, University of Manchester, PO Box 88, Faraday Building, Sackville Street, M60 1QD, Manchester, UK
150 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...