MINLP Based Retrieval of Generalized Cases
Identifieur interne : 000176 ( LNCS/Analysis ); précédent : 000175; suivant : 000177MINLP Based Retrieval of Generalized Cases
Auteurs : Alexander Tartakovski [Allemagne] ; Martin Schaaf [Allemagne] ; Rainer Maximini [Allemagne] ; Ralph Bergmann (informaticien) [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.
Descripteurs français
- Pascal (Inist)
- Automatisation, Composant logiciel, Intelligence artificielle, Méthode optimisation, Optimisation, Problème non linéaire, Programmation mathématique, Programmation mixte, Programmation non linéaire, Programmation partiellement en nombres entiers, Raisonnement basé sur cas, Réponse temporelle, Similitude, Temps réponse.
- Wicri :
- topic : Automatisation, Intelligence artificielle.
English descriptors
- KwdEn :
Abstract
Abstract: The concept of generalized cases has been proven useful when searching for configurable and flexible products, for instance, reusable components in the area of electronic design automation. This paper addresses the similarity assessment and retrieval problem for case bases consisting of traditional and generalized cases. While approaches presented earlier were restricted to continuous domains, this paper addresses generalized cases defined over mixed, continuous and discrete, domains. It extends the view on the similarity assessment as a nonlinear optimization problem (NLP) towards a mixed integer nonlinear optimization problem (MINLP), which is an actual research topic in mathematical optimization. This is an important step because most real world applications require mixed domains for the case description. Furthermore, we introduce two optimization-based retrieval methods that operate on a previously created index structure, which restricts the retrieval response time significantly.
Url:
DOI: 10.1007/978-3-540-28631-8_30
Affiliations:
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<front><div type="abstract" xml:lang="en">Abstract: The concept of generalized cases has been proven useful when searching for configurable and flexible products, for instance, reusable components in the area of electronic design automation. This paper addresses the similarity assessment and retrieval problem for case bases consisting of traditional and generalized cases. While approaches presented earlier were restricted to continuous domains, this paper addresses generalized cases defined over mixed, continuous and discrete, domains. It extends the view on the similarity assessment as a nonlinear optimization problem (NLP) towards a mixed integer nonlinear optimization problem (MINLP), which is an actual research topic in mathematical optimization. This is an important step because most real world applications require mixed domains for the case description. Furthermore, we introduce two optimization-based retrieval methods that operate on a previously created index structure, which restricts the retrieval response time significantly.</div>
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