Providing high-level control and expert assistance in the user interface presentation design
CHI '93 Proceedings of the INTERACT '93 and CHI '93 Conference on Human Factors in Computing Systems
Java Look & Feel Design Guidelines
Java Look & Feel Design Guidelines
Interaction Design
Towards Creative Evolutionary Systems with Interactive Genetic Algorithm
Applied Intelligence
User interface design with matrix algebra
ACM Transactions on Computer-Human Interaction (TOCHI)
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Generative UI design in SAPI project
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Optimizing hierarchical menus by genetic algorithm and simulated annealing
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology
Flippable user interfaces for internationalization
Proceedings of the 3rd ACM SIGCHI symposium on Engineering interactive computing systems
Optimization of weighted vector directional filters using an interactive evolutionary algorithm
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
MenuOptimizer: interactive optimization of menu systems
Proceedings of the 26th annual ACM symposium on User interface software and technology
| Hi-index | 0.00 |
We attack the problem of user fatigue by using an interactive genetic algorithm to evolve user interfaces in the XUL interface definition language. The interactive genetic algorithm combines a set of computable user interface design metrics with subjective user input to guide the evolution of interfaces. Our goal is to provide user interface designers with a tool that can be used to explore innovation and creativity in the design space of user interfaces and make it easier for end-users to further customize their user interface without programming knowledge. User interface specifications are encoded as individuals in an interactive genetic algorithm's population and their fitness is computed from a weighted combination of user interface design guidelines and user input. This paper shows that we can reduce human fatigue in interactive genetic algorithms (the number of choices needing to be made by the designer), by 1) only asking the user to pick two user interfaces from among ten shown on the display and 2) by asking the user to make the choice once every t generations.