A guided search genetic algorithm using mined rules for optimal affective product design
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...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Taylor and Francis Ltd
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/15382 |
| _version_ | 1848748877727924224 |
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| author | Fung, K.Y. Kwong, C. Chan, Kit Yan Jiang, H. |
| author_facet | Fung, K.Y. Kwong, C. Chan, Kit Yan Jiang, H. |
| author_sort | Fung, K.Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:12:02Z |
| format | Journal Article |
| id | curtin-20.500.11937-15382 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:12:02Z |
| publishDate | 2014 |
| publisher | Taylor and Francis Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-153822017-09-13T13:41:02Z A guided search genetic algorithm using mined rules for optimal affective product design Fung, K.Y. Kwong, C. Chan, Kit Yan Jiang, H. new product development customer satisfaction guided search genetic algorithms Affective design 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. 2014 Journal Article http://hdl.handle.net/20.500.11937/15382 10.1080/0305215X.2013.823196 Taylor and Francis Ltd fulltext |
| spellingShingle | new product development customer satisfaction guided search genetic algorithms Affective design Fung, K.Y. Kwong, C. Chan, Kit Yan Jiang, H. A guided search genetic algorithm using mined rules for optimal affective product design |
| title | A guided search genetic algorithm using mined rules for optimal affective product design |
| title_full | A guided search genetic algorithm using mined rules for optimal affective product design |
| title_fullStr | A guided search genetic algorithm using mined rules for optimal affective product design |
| title_full_unstemmed | A guided search genetic algorithm using mined rules for optimal affective product design |
| title_short | A guided search genetic algorithm using mined rules for optimal affective product design |
| title_sort | guided search genetic algorithm using mined rules for optimal affective product design |
| topic | new product development customer satisfaction guided search genetic algorithms Affective design |
| url | http://hdl.handle.net/20.500.11937/15382 |