We propose Interactive Differential Evolution (IDE) based on paired comparisons for reducing user fatigue and evaluate its convergence speed in comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User interface and convergence performance are two big keys for reducing Interactive Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE, users of the proposed IDE and tournament IGA do not need to compare whole individuals each other but compare pairs of individuals, which largely decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate another factor, IEC convergence performance, using IEC simulators and show that our proposed IDE converges significantly faster than IGA and tournament IGA, i.e. our proposed one is superior to others from both user interface and convergence performance points of view.
Evolutionary Algorithms; Differential Evolution; Interactive Evolutionary Computation, Paired Comparison, Gaussian Mixture ModelI.
International audienceIn a collaborative computer-supported engineering environment, the interoperation of various applications will need a representation that goes beyond the current geometry-based representation, which is inadequate for capturing semantic information. The primary purpose of this study is to discuss a semantically based information exchange protocol that will facilitate seamless interoperability among current and next generation computer-aided design systems (CAD) and between CAD and other systems that use product data. An ontological approach is described to integrating computer-aided design (CAD) and computer-aided process planning (CAPP). Two commercial software applications are used to demonstrate the approach. This involves the development of a shared ontology and domain specific ontologies in the Knowledge Interchange Format (KIF) language. Domain specific ontologies — which are feature-based — are developed after a detailed analysis of the CAD and the CAPP software. Mapping between the domain ontologies and the shared ontology is achieved by several mapping rules. The approach is validated by using a variety of parts
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