A comparison of selection time from walking and pull-down menus
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Structural analysis of hypertexts: identifying hierarchies and useful metrics
ACM Transactions on Information Systems (TOIS)
Beyond Fitts' law: models for trajectory-based HCI tasks
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Java look and feel design guidelines: advanced topics
Java look and feel design guidelines: advanced topics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Examining a metric for predicting the accessibility of information within hypertext structures
Examining a metric for predicting the accessibility of information within hypertext structures
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improving menu interaction: a comparison of standard, force enhanced and jumping menus
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A predictive model of menu performance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Generative UI design in SAPI project
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Improving Access to Services through Intelligent Contents Adaptation: The SAPI Framework
WSKS '09 Proceedings of the 2nd World Summit on the Knowledge Society: Visioning and Engineering the Knowledge Society. A Web Science Perspective
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Menu systems are key components in modern graphical user interfaces (GUIs), either for traditional desktop applications, or for the latest web applications. The design of interface layout must consider different aspects resulting in a trade-off between often conflicting requirements. This trade-off is aimed at making effective use of interfaces in order to meet user preferences and to conform to standard guidelines at the same time. Assuming we are able to quantify such a trade-off, the problem of finding a menu system able to maximize it figures as a combinatorial optimization problem. In this paper we investigate the application of genetic algorithms as a viable approach to identifying solutions that can be used as a starting point for further fine-tuning.