Linked Life Data

14629865

Source:http://linkedlifedata.com/resource/pubmed/id/14629865

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2003-11-21
pubmed:abstractText
Almost all approaches to multiobjective optimization are based on Genetic Algorithms (GAs), and implementations based on Evolution Strategies (ESs) are very rare. Thus, it is crucial to investigate how ESs can be extended to multiobjective optimization, since they have, in the past, proven to be powerful single objective optimizers. In this paper, we present a new approach to multiobjective optimization, based on ESs. We call this approach the Multiobjective Elitist Evolution Strategy (MEES) as it incorporates several mechanisms, like elitism, that improve its performance. When compared with other algorithms, MEES shows very promising results in terms of performance.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1063-6560
pubmed:author
pubmed:issnType
Print
pubmed:volume
11
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
417-38
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
An adaptive sharing elitist evolution strategy for multiobjective optimization.
pubmed:affiliation
Departamento de Produção e Sistemas, Universidade do Minho, Campus de Gualtar, 4710 Braga, Portugal. lac@dps.uminho.pt
pubmed:publicationType
Journal Article