Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Interactive Evolutionary Computation as Humanized Computational Intelligence Technology
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
On Generating HTML Style Sheets with an Interactive Genetic Algorithm Based on Gene Frequencies
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
User interface design with matrix algebra
ACM Transactions on Computer-Human Interaction (TOCHI)
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
Magellan, an evolutionary system to foster user interface design creativity
Proceedings of the 2nd ACM SIGCHI symposium on Engineering interactive computing systems
23rd French Speaking Conference on Human-Computer Interaction
Examples galleries generated by interactive genetic algorithms
Procedings of the Second Conference on Creativity and Innovation in Design
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Graphical user interface design is a time consuming, expensive, and complex software design process. User interface design is both art and science in that we use both objective and subjective design metrics to evaluate interfaces. An automated process that relies on both subjective and objective metrics to guide the evolution of effective, personalized user interfaces could significantly change current GUI development and maintenance practice. This paper uses an interactive genetic algorithm to evolve XUL user interface layouts by combining objective and subjective metrics. The genetic algorithm encodes expert knowledge from prominent usability guidelines as objective heuristics. Further, the graphical user interface developer (or user!) biases and guides the evolution of the interfaces by subjectively evaluating and selecting the.best. and.worst. interfaces from a small set of displayed interface prototypes. We explore how the selection of individuals from the population to be displayed to the user for subjective evaluation affects the convergence of the genetic algorithm and show that our methodology can produce effective interfaces that reflect subjective user-preferred aesthetics.