ABSTRACT
In this paper, we propose a genetic algorithm formal framework to optimize character location on a soft keyboard. This method is described regardless of the language and layout used and can then easily be adapted to any language and layout. In this scope, we present a measure, based on Mackenzie's model, to estimate the performances of the best characters in a given layout. We apply our method to common English language and two different layouts (hexagonal and rectangular layout) in order to compare with FITALY, OPTI or Metropolis keyboards. In all configurations, our method has the best performance.
- Darwin, C. On the Origin of Species by means of natural selection, or the Preservations of favored races in the struggle of life. 1859Google Scholar
- Fitts, P.M. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47 (1954), 381--391Google ScholarCross Ref
- Goldberg, D.E. Genetic Algorithms in Search. Optimisation and Machine Learning, 1989 Google ScholarDigital Library
- MacKenzie, I.S., and Zhang S.X. The design and evaluation of a high-performance soft keyboard. CHI 99, ACM Press (1999), 25--31 Google ScholarDigital Library
- MacKenzie, I.S., and Soukoreff, R. W. Text entry for mobile computing: Models and methods, theory and practice. Human-Computer Interaction, 17 (2002), 147--198Google ScholarCross Ref
- Mayzner, M.S. ans Tresselt. Tables of single-letter and digram frequency counts for various word-length and letter-position combinations. Psychonomic Monograph Supplements, 1965.1 (2), 13--32Google Scholar
- Soukoreff, R. W., and MacKenzie, I.S. Theoretical upper and lower bounds on typing speed using a stylus and soft keyboard. Behavior & Information Technology, 14 (1995), 370--379Google ScholarCross Ref
- Zhai, S., Hunter, M., and Smith, B. A. The Metropolis keyboard - An exploration of quantitative techniques for virtual keyboard design. Proc. UIST 2000, CHI Letters 2(2), ACM Press (2000), 119--128 Google ScholarDigital Library
Index Terms
- Genetic algorithm to generate optimized soft keyboard
Recommendations
An improved genetic algorithm with conditional genetic operators and its application to set-covering problem
The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an ...
Hybrid Taguchi-genetic algorithm for global numerical optimization
In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is proposed to solve global numerical optimization problems with continuous variables. The HTGA combines the traditional genetic algorithm (TGA), which has a powerful global exploration capability,...
Solving Japanese nonograms by Taguchi-based genetic algorithm
A Taguchi-based genetic algorithm (TBGA) is proposed to solve Japanese nonogram puzzles. The TBGA exploits the power of global exploration inherent in the traditional genetic algorithm (GA) and the abilities of the Taguchi method in efficiently ...
Comments