Discovering generalized design knowledge using a multi-objective evolutionary algorithm with generalization operators. (1st April 2020)

• Record Type: Journal Article
• Authors: Bang, Hyunseung; Selva, Daniel
• Is Part Of: Expert systems with applications. Volume 143(2020)
• Publication Date: 2020-04-01
• Subjects: Design space exploration -- Knowledge discovery -- Feature extraction -- Data mining -- Multi-objective evolutionary algorithm -- Adaptive operator selection; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.113025
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Design knowledge is extracted using multi-objective evolutionary algorithm. Generalization of concepts is used to make design knowledge more compact. Different generalization strategies can be combined to improve the performance. Abstract: The early-phase design of complex systems is a challenging task, as a decision maker has to take into account the intricate relationships among different design variables. A popular way to help decision makers easily identify important design features is to use data mining. However, many of the existing algorithms output design features that are too complex (e.g., conjunction of many literals with unrelated predicates), making it difficult for a user to understand, remember, and apply these features to find better designs. In this paper, we introduce a new data mining method that extracts compact design features through knowledge generalization. The proposed method performs a search over the space of features using a multi-objective evolutionary algorithm that contains a set of generalization operators in addition to conventional evolutionary operators. Both variables and feature types are generalized by using an ontology defining a set of domain-specific concepts and relationships. Generalization leads to more compact and insightful features, as generalized knowledge encompasses wider concepts. A comparative experiment is conducted on a real-world system architecting problem to demonstrate the gain in compactness of the extracted features without significant reductions in predictive power.