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dc.contributor.authorFung, K.Y.
dc.contributor.authorKwong, C.
dc.contributor.authorChan, Kit Yan
dc.contributor.authorJiang, H.
dc.identifier.citationFung, K.Y. and Kwong, C.K. and Chan, Kit Yan and Jiang, H. 2014. A guided search genetic algorithm using mined rules for optimal affective product design. Engineering Optimization. 46 (8): pp. 1094-1108.

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.

dc.publisherTaylor and Francis Ltd
dc.subjectnew product development
dc.subjectcustomer satisfaction
dc.subjectguided search genetic algorithms
dc.subjectAffective design
dc.titleA guided search genetic algorithm using mined rules for optimal affective product design
dc.typeJournal Article
dcterms.source.titleEngineering Optimization

This is an Author's Accepted Manuscript of an article published in Engineering Optimization, 2014, copyright Taylor & Francis, available online at: <a href=""></a>

curtin.accessStatusOpen access

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