What Kind of UIs do Users Evolve?

Interactive Genetic Algorithms for User Interface DesignJuan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu

Evolutionary Computing Systems Lab, Department of Computer Science and EngineeringUniversity of Nevada, Reno

{quiroz, anilk, sushil, dascalus}@cse.unr.edu

Interactive Genetic Algorithms for User Interface DesignJuan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu

Evolutionary Computing Systems Lab, Department of Computer Science and EngineeringUniversity of Nevada, Reno

{quiroz, anilk, sushil, dascalus}@cse.unr.edu

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.

How Do We Encode UIs in the IGA?

Interactive Genetic Algorithms for User Interface DesignJuan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu

Evolutionary Computing Systems Lab, Department of Computer Science and EngineeringUniversity of Nevada, Reno

{quiroz, anilk, sushil, dascalus}@cse.unr.edu

Interactive Genetic Algorithms

Interactive Genetic Algorithms for User Interface DesignJuan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu

Evolutionary Computing Systems Lab, Department of Computer Science and EngineeringUniversity of Nevada, Reno

{quiroz, anilk, sushil, dascalus}@cse.unr.edu

How Does the User Provide Feedback?

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.

Interactive Evolutionary Approach for UI Design

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 designs and to provide creativity and insight.

Future Work1. Further user studies2. Vary value of t through IGA run,

instead of using a constant value of t during a run

3. Expand widget encoding to support widget coupling and high level spatial relationships

4. Explore creative layout designs

Generation 0 Generation 30

Conclusions1. Users effectively guide the evolution

of UIs2. Higher values of “t” can reduce user

fatigue by accelerating convergence3. We see a drop in average fitness

performance associated with time steps when the user provides input

Conclusions1. Users effectively guide the evolution

of UIs2. Higher values of “t” can reduce user

fatigue by accelerating convergence3. We see a drop in average fitness

performance associated with time steps when the user provides input

Future Work1. Conduct further user studies2. Vary value of t through IGA run,

instead of using a constant value of t during a run

3. Expand widget encoding to support widget coupling and high level spatial relationships

4. Explore creative layout designs

Conclusions1. Users effectively guide the evolution

of UIs2. Higher values of “t” can reduce user

fatigue by accelerating convergence3. We see a drop in average fitness

performance associated with time steps when the user provides input

Coded Guideline Metrics?1. High contrast between the background color

and the color of widgets2. Low contrast between widget colors3. Left and right alignment of widgets—our

organization of widgets into a grid construct implicitly enforces the alignment of widgets

How Do We Evaluate the Fitness of UIs?

1. We display a small subset of the IGA population to be evaluated by the user.

2. The user then picks the UI the user likes the least and the UI the user likes the most.

3. We compute the subjective fitness by comparing individuals in the population to the user-selected “best” and “worst” UIs.

4.We compute the objective fitness by checking conformance to coded guideline metrics.

5.The fitness of a UI is computed as a linear weighted sum of its objective and subjective fitness components.

What is t?We explore the effects of varying the frequency of user input every tth generation. With higher values of t we can accelerate the population convergence and reduce user fatigue by asking for less user input.

How Does t Affect the Fitness Performance?

For all three users, using a value of t greater than 1 results in the best fitness performance. Thus, asking for less user input reduces user fatigue without compromising fitness performance. The average fitness shows drops in fitness associated with the time steps on which the users provide feedback.

CEC 2007