Human Guided Evolution of XUL User Interfaces
Juan C. Quiroz, Sergiu M. Dascalu, and Sushil J. Louis
Evolutionary Computing Systems Lab
University of Nevada, Reno
{quiroz, dascalus, sushil}@cse.unr.edu
Evolution Environment
Motivation Experiments
Graphical user interface design is a time Who do we display from the population
consuming, expensive, and complex software for user evaluation? Should we display
design process. the top n individuals, a mix of the best
and worst n individuals, or should we
User interface design is both art and science in that display n random individuals?
we use both objective and subjective design metrics
to evaluate interfaces. We have a greedy simulated user with
preference for blue UIs guide the
An automated process that relies on both subjective evolution for this experiment.
and objective metrics to guide the evolution of
effective, personalized user interfaces could
significantly change current GUI development and
maintenance practice.
Results
2. Displaying the top n individuals for user evaluation results in better and faster
convergence to desired user interfaces.
3.The evolved user interfaces both reflect the assumed user’s preference, for the color
Interactive Evolutionary Approach blue, and the encoded guidelines of style.
We use an interactive genetic algorithm (IGA) to help
user interface designers explore the space of UIs.
User interface designers are guided by both
objective metrics, obtained from guidelines of style,
and subjective metrics, obtained from the designer’s
expertise, intuition, and emotions.
Our interactive genetic algorithm combines both
objective and subjective heuristics to evolve user
interfaces in order to expose the designer to various Fitness convergence of the best individuals when varying Convergence to blue UIs of the best individuals when varying
designs and to provide creativity and insight. who from the population is displayed for user evaluation. who from the population is displayed for user evaluation.
Fitness Evaluation
2. We display a small subset of the IGA population
to be evaluated to the user.
3. The user then picks the UI the user likes the least
and the UI the user likes the most.
4. We compute the subjective fitness by comparing
individuals in the population to the user selected Fitness convergence of the average individuals when varying Convergence to blue UIs of the average individuals when
“best and user selected “worst”. who from the population is displayed for user evaluation. varying who from the population is displayed for user
evaluation.
5.We compute the objective fitness by checking
conformance to coded guideline metrics.
6.The fitness of a UI is computed as a linear
weighted sum of its objective and subjective User Interface Encoding
fitness components.
Interactive Genetic Algorithms
User interfaces evolved by a simulated, greedy user with preference for the color blue.
Future Work
2. Conduct user studies to asses the utility of
the tool.
Guidelines Enforced 3. Explore various representations and color
models that are more natural and intuitive to
2. High contrast between the background color
users.
and the color of widgets
4. Incorporate the ability to specify high level
3. Low contrast between widget colors
spatial relationships between widgets is
4. Left and right alignment of widgets. Our
also needed, such as the coupling of a
organization of widgets into a grid construct
textbox with a label.
implicitly enforces the alignment of widgets.
User interfaces evolved by an actual user after 30 generations.
Acknowledgments: This material is based in part upon work supported by the Office of Naval Research under contract number N00014-03-1-0104 and in part upon work supported by the National Science Foundation under Grant No. 0447416.
CHI 2007