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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hardman, N. Colombi, J. Jacques, D. Hill, R. Miller, J. |
| Copyright Year | 2009 |
| Description | Author affiliation: 711th Human Performance Wing, Wright-Patterson, Ohio 45433 USA (Miller, J.) || Air Force Institute of Technology, Wright-Patterson, Ohio 45433 USA (Hardman, N.; Colombi, J.; Jacques, D.; Hill, R.) |
| Abstract | Studies have established that computer user interface (UI) design is a primary contributor to people's experiences with modern technology; however, current UI development remains more art than quantifiable science. In this paper, we study the use of search algorithms to predict optimal display layouts early in system design. This has the potential to greatly reduce the cost and improve the quality of UI development. Specifically, we demonstrate a hybrid genetic algorithm and pattern search optimization process that makes use of human performance modeling to quantify known design principles. We show how this approach can be tailored by capturing contextual factors in order to properly seed and tune the genetic algorithm. Finally, we demonstrate the ability of this process to discover superior layouts as compared to manual qualitative methods. |
| Starting Page | 462 |
| Ending Page | 467 |
| File Size | 829499 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424427932 |
| ISSN | 1062922X |
| DOI | 10.1109/ICSMC.2009.5346789 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-10-11 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Genetic algorithms User interfaces Algorithm design and analysis Application software Computer interfaces Art Prediction algorithms Computer displays Costs Design optimization human-machine interface User interface design human performance modeling genetic algorithm pattern search human-computer interaction |
| Content Type | Text |
| Resource Type | Article |
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