A Deep Genetic Method for Keyboard Layout Optimization

Keren Nivasch, Amos Azaria

Abstract: The QWERTY keyboard layout that is commonly used today was designed, over 100 years ago, for typewriters rather than for modern keyboards. Over the decades, many people have tried manually to come up with better layout designs. Recently, researchers have also attempted to automatically find a better keyboard layout by using advanced algorithms. In this paper we propose the use of deep learning with a genetic algorithm for finding improved keyboard layouts. We also show that using an appropriate crossover routine, instead of the crossover routine previously used in the literature, significantly improves the performance of the genetic algorithm. Our method, which we call MKLOGA, produces a keyboard layout that outperforms previous layouts, including those found by other algorithms, according to the realistic typing effort model of carpalx. We provide an installation of our keyboard layout. MKLOGA might also be useful for developing good layouts for languages other than English, and possibly for other domains in which objects must be placed in predefined locations.