2021-03-24
AUTHORSTomoaki Takagi , Keiki Takadama , Hiroyuki Sato
ABSTRACTThis work proposes a method to estimate the Pareto front even in areas without objective vectors in the objective space. For the Pareto front approximation, we use a set of non-dominated points, objective vectors, in the objective space. To finely approximate the Pareto front, we need to increase the number of objective vectors. It is worth to estimate the Pareto front with a limited number of objective vectors. The proposed method uses the Kriging approximation and estimates the Pareto front using the unit hyperplane in the objective space. In the experiment using representative simple and complicated Pareto fronts derived from the DTLZ family, we visually show the estimation quality of the proposed method. Also, we show that the shape of the Pareto front and the distribution of sample objective vectors affect the estimation quality. More... »
PAGES126-138
Evolutionary Multi-Criterion Optimization
ISBN
978-3-030-72061-2
978-3-030-72062-9
http://scigraph.springernature.com/pub.10.1007/978-3-030-72062-9_11
DOIhttp://dx.doi.org/10.1007/978-3-030-72062-9_11
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1136614969
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/2008/01",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Mathematical Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Statistics",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan",
"id": "http://www.grid.ac/institutes/grid.266298.1",
"name": [
"The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan"
],
"type": "Organization"
},
"familyName": "Takagi",
"givenName": "Tomoaki",
"id": "sg:person.015022623513.33",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015022623513.33"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan",
"id": "http://www.grid.ac/institutes/grid.266298.1",
"name": [
"The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan"
],
"type": "Organization"
},
"familyName": "Takadama",
"givenName": "Keiki",
"id": "sg:person.012774267611.99",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012774267611.99"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan",
"id": "http://www.grid.ac/institutes/grid.266298.1",
"name": [
"The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan"
],
"type": "Organization"
},
"familyName": "Sato",
"givenName": "Hiroyuki",
"id": "sg:person.07750750604.05",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07750750604.05"
],
"type": "Person"
}
],
"datePublished": "2021-03-24",
"datePublishedReg": "2021-03-24",
"description": "This work proposes a method to estimate the Pareto front even in areas without objective vectors in the objective space. For the Pareto front approximation, we use a set of non-dominated points, objective vectors, in the objective space. To finely approximate the Pareto front, we need to increase the number of objective vectors. It is worth to estimate the Pareto front with a limited number of objective vectors. The proposed method uses the Kriging approximation and estimates the Pareto front using the unit hyperplane in the objective space. In the experiment using representative simple and complicated Pareto fronts derived from the DTLZ family, we visually show the estimation quality of the proposed method. Also, we show that the shape of the Pareto front and the distribution of sample objective vectors affect the estimation quality.",
"editor": [
{
"familyName": "Ishibuchi",
"givenName": "Hisao",
"type": "Person"
},
{
"familyName": "Zhang",
"givenName": "Qingfu",
"type": "Person"
},
{
"familyName": "Cheng",
"givenName": "Ran",
"type": "Person"
},
{
"familyName": "Li",
"givenName": "Ke",
"type": "Person"
},
{
"familyName": "Li",
"givenName": "Hui",
"type": "Person"
},
{
"familyName": "Wang",
"givenName": "Handing",
"type": "Person"
},
{
"familyName": "Zhou",
"givenName": "Aimin",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-030-72062-9_11",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-030-72061-2",
"978-3-030-72062-9"
],
"name": "Evolutionary Multi-Criterion Optimization",
"type": "Book"
},
"keywords": [
"objective vector",
"objective space",
"Pareto front",
"unit hyperplane",
"estimation quality",
"non-dominated points",
"Pareto front approximation",
"complicated Pareto fronts",
"DTLZ family",
"Kriging approximation",
"front approximation",
"approximation",
"hyperplane",
"space",
"vector",
"front",
"estimation",
"set",
"number",
"limited number",
"distribution",
"point",
"shape",
"work",
"experiments",
"quality",
"family",
"area",
"method"
],
"name": "Pareto Front Estimation Using Unit Hyperplane",
"pagination": "126-138",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1136614969"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-030-72062-9_11"
]
}
],
"publisher": {
"name": "Springer Nature",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-030-72062-9_11",
"https://app.dimensions.ai/details/publication/pub.1136614969"
],
"sdDataset": "chapters",
"sdDatePublished": "2022-05-10T10:41",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/chapter/chapter_207.jsonl",
"type": "Chapter",
"url": "https://doi.org/10.1007/978-3-030-72062-9_11"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
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.10.1007/978-3-030-72062-9_11'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-72062-9_11'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-72062-9_11'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-72062-9_11'
This table displays all metadata directly associated to this object as RDF triples.
133 TRIPLES
23 PREDICATES
54 URIs
47 LITERALS
7 BLANK NODES