Incorporating domain-specific knowledge into a genetic algorithm to implement case-based reasoning adaptation

Passone, Sara; Chung, Pei-Jung; Nassehi, V.

doi:10.1016/j.knosys.2005.07.007

Cited by 35 publications

(12 citation statements)

References 2 publications

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“…However, Passone et al (2006) pointed out that insufficient knowledge can badly affect the selection of an appropriate learning algorithm and its performance. To overcome the shortcoming, some researchers proposed new knowledge-light methods which not only get knowledge from the CBR system itself, but also regard that knowledge as the starting point for adaptation processes, and find new knowledge through other ML methods (Minor et al, 2014;Assali et al, 2013;Goh and Chua, 2010).…”

Section: Knowledge-light Case Adaptationmentioning

confidence: 99%

Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation

Qi¹,

Hu²,

Peng³

2015

Engineering Applications of Artificial Intelligence

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Section: Knowledge-light Case Adaptationmentioning

confidence: 99%

Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation

Qi¹,

Hu²,

Peng³

2015

Engineering Applications of Artificial Intelligence

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“…In some applications of CBR, similarity of stored cases is assessed in terms of their surface features, which are parts of their description typically represented using attribute–value pairs. Various methods exist: k -nearest neighbor ( k -NN) based on Euclidean distance; mixed neural networks (Chang et al, 2012); fuzzy logic (Begum et al, 2009); and genetic algorithms (Passone et al, 2006). Structured cases often require knowledge-intensive matching algorithms to assess structural similarity.…”

Section: Related Workmentioning

confidence: 99%

Analysis of semantic features in free-form objects reconstruction

Trifunović

Stojković

Trajanović

et al. 2015

AIEDAM

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One of the biggest challenges associated with design and digital reconstruction of free forms comes from uniqueness and unrepeatability of these shapes. During digital reconstruction of these forms, the designer has to choose the right set of geometric features and then compose them in a way that will enable the most accurate reconstruction of the geometry. While doing this, the designer primarily relies on personal experience gained through work with free-form objects of similar geometry. In our opinion, the analysis of free-form objects geometry should rely upon semantic interpretation of their geometric and other features, and the greatest challenge of automation of digital reconstruction and free-form object design in general is closely related to automation of semantic interpretation of geometric and other free-form object features. In this paper, a case of chest bone implant digital reconstruction is presented, where a new semantic model called the active semantic model was used for modeling the meaning of geometric elements, that is, the semantic features of a free-form object. The active semantic model and its analogy-based reasoning algorithms have shown themselves as applicable for the automation of semantic interpretation of the unique, unrepeatable, and unpredictable forms of chest bone. Moreover, this semantic model showed the potential to help automate selecting and composing of geometric features for efficient digital reconstruction of the geometry of free forms.

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“…Many researchers have worked in this domain, and various retrieval techniques have been developed, which range from classical methods, such as Schaaf [20], to more sophisticated methods that mix neural networks [21,22], genetic algorithms [23][24][25], fuzzy ant colony systems [26] or fuzzy logic [27][28][29][30]. Another technique used to improve accuracy in CBR retrieval is the approach of attribute weighting.…”

Section: Related Research: Cbr and Ccmmentioning

confidence: 99%

Analyzing the Effect of Collaborative Cost Management in Supply Chain by Case-Based Reasoning

Fu¹,

Fu²

2013

JSW

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Applying case-based reasoning (CBR) to analyze the effect of collaborative cost management (CCM) in supply chain is a new research topic. This paper illustrates a new method which combining four steps of CCM and procedures of CBR with fuzzy inference model. The method improves the traditional similarity assessment and obtains the most suitable case to solve new problems. Moreover, this study gives a new definition of information set on the CCM problem and emphasizes self-learning in CBR for CCM in order to make the selected case solution more suitable and obtain the more collaborative effect value of CCM.

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Incorporating domain-specific knowledge into a genetic algorithm to implement case-based reasoning adaptation

Cited by 35 publications

References 2 publications

Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation

Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation

Analysis of semantic features in free-form objects reconstruction

Analyzing the Effect of Collaborative Cost Management in Supply Chain by Case-Based Reasoning

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