The problem of computational design for menu systems has been addressed in some specific cases such as the linear menu (list). The classical approach has been to model this problem as an assignment task, where commands are assigned to menu positions while optimizing for users’ selection performance and grouping of associated items. However, we show that this approach fails with larger, hierarchically organized menus because it does not take into account the ways in which users navigate hierarchical structures. This paper addresses the computational menu design problem by presenting a novel integer programming formulation that yields usable, well-ordered command hierarchies from a single model. First, it introduces a novel objective function based on information foraging theory, which minimizes navigation time in a hierarchical structure. Second, it models the hierarchical menu design problem as a combination of the exact set covering problem and the assignment problem, organizing commands into ordered groups of ordered groups. The approach is efficient for large, representative instances of the problem. In a controlled usability evaluation, the performance of computationally designed menus was ∼25% faster to use than existing commercial designs. We discuss applications of this approach for personalization and adaptation.

Graphical user interfaces (GUIs) have gained primacy among the means of interacting with computing systems, thanks to the way they leverage human perceptual and motor capabilities. However, the design of GUIs has mostly been a manual activity. To design a GUI, the designer must select its visual, spatial, textual, and interaction properties such that the combination strikes a balance among the relevant human factors. While emerging computational-design techniques have addressed some problems related to grid layouts, no general approach has been proposed that can also produce good and complete results covering color-related decisions and other nonlinear design objectives. Evolutionary algorithms are promising and demonstrate good handling of similar problems in other conditions, genetic operators, depending on how they are designed. But even these approaches struggle with elements' overlap and hence produce too many infeasible candidate solutions. This paper presents a new approach based on grid-based genetic operators demonstrated in a non-dominated sorting genetic algorithm (NSGA-III) setting. The operators use grid lines for element positions in a novel manner to satisfy overlap-related constraints and intrinsically improve the alignment of elements. This approach can be used for crossovers and mutations. Its core benefit is that all the solutions generated satisfy the no-overlap requirement and represent well-formed layouts. The new operators permit using genetic algorithms for increasingly realistic task instances, responding to more design objectives than could be considered before. Specifically, we address grid quality, alignment, selection time, clutter minimization, saliency control, color harmony, and grouping of elements.

Computing systems have been facing severe technology challenges in recent years with regard to power consumption, circuit reliability, and high performance. For many years, the issues of power consumption and performance have been addressed with the use of technology scaling.However, as Dennard’s scaling tends toward an end, it has become difficult to further improve the performance under the same power constraints. In addition to power, reliability also becomes a critical issue when the feature size of the complementary metal-oxide–semiconductor (CMOS) technology is reduced below 7 nm. Thus, ensuring the complete accuracy of the signal has become increasingly challenging in recent years.

The graphical user interface (GUI) has become the prime means for interacting with computing systems. It leverages human perceptual and motor capabilities for elementary tasks such as command exploration and invocation, information search, and multitasking. For designing a GUI, numerous interconnected decisions must be made such that the outcome strikes a balance between human factors and technical objectives. Normally, design choices are specified manually and coded within the software by professional designers and developers. This article surveys combinatorial optimization as a flexible and powerful tool for computational generation and adaptation of GUIs. As recently as 15 years ago, applications were limited to keyboards and widget layouts. The obstacle has been the mathematical definition of design tasks, on the one hand, and the lack of objective functions that capture essential aspects of human behavior, on the other. This article presents definitions of layout design problems as integer programming tasks, a coherent formalism that permits identification of problem types, analysis of their complexity, and exploitation of known algorithmic solutions. It then surveys advances in formulating evaluative functions for common design-goal foci such as user performance and experience. The convergence of these two advances has expanded the range of solvable problems. Approaches to practical deployment are outlined with a wide spectrum of applications. This article concludes by discussing the position of this application area within optimization and human-computer interaction research and outlines challenges for future work.