Multi-Factor User Interface Components Layout Problem with Genetic Algorithm (2012)

INTRODUCTION The well-designed user interface makes an interactive system effective enabling user to concentrate on their work, exploration, or pleasure. The ability to perform routine activities quickly, effortlessly, and without conscious awareness is called automaticity (Logan, 1988). Automaticity is relevant and important construct for the study of human-computer interaction in general and for the study of direct manipulation of interfaces in particular. Some studies show that the total time required to complete the task is a direct manipulation interface will be less than in a menu based interface (Lim et al., 1996). Within the computer science, there is a growing awareness of the need for great attention to human factors issues. The business cases for human factors in computer and information systems are strong as demonstrated by many successful products whose advantage lay in their superior user interface. There is a one-to-one relationship between the facilities layout problem in a manufacturing plant and the user interface components layout problem in human-computer interface design (Peer et al., 2004). The quadratic assignment problem (QAP) formulation for assigning 'n' facilities to 'n' mutually exclusive locations is the most typical model used in manufacturing or interactive service systems. A classical QAP is formulated handling qualitative (subjective) factor(s) and quantitative (objective) factor(s) to obtain the layouts, using construction procedure or improvement procedure. The multi-criteria facilities layout model handles a number of criteria values associated in the multiple qualitative and quantitative factors with the objective function of the quadratic assignment problem (QAP). Harmonosky and Tothero (1992) presented a quadratic assignment model to handle the multiple qualitative and quantitative factors in the same manner when applying them into the objective function. Then, a construction procedure was proposed, in which the pair of facilities with least composite criterion value is selected to place far apart in the layout to obtain the initial layouts. The resulting initial layouts may be improved further by using the improvement procedures. Peer et al. (2003) used the multi-factor facilities layout model of Sharma and Peer (2005) in which multiple qualitative and quantitative factors were handled in different manner separately to obtain the alternate layouts of the user interface components of the example task under consideration. Then, the search techniques such as simulated annealing (Suresh and Sahu, 1993), ant colony optimization technique (Baykasoglu, 2006), genetic algorithm, etc. are used to obtain the best layouts. Good heuristic techniques are necessary to obtain the best layouts due to its high computational complexity. Modern heuristic techniques, namely genetic algorithms (Goldberg, 1989; Michalewicz, 1996), taboo search (Kaku and Mazzola, 1997), simulated annealing (Baykasoglu and Gindy, 2001) and ant colony optimization (Baykasoglu et al., 2006), can be good candidates for this problem. Genetic algorithms (GAs) have been applied extensively to solve many real-world problems. Conway and Venkataramanan (1994) examined the suitability of genetic algorithm for the dynamic layout problem (DLP). Tate and Smith (1995) used a GA to solve QAP. Suresh et al. (1995) also proposed a GA for obtaining efficient layouts. Balakrishnan and Cheng (2000) presented a GA for DLP. Deb et al. (2001) and Deb and Bhattacharyya (2003, 2005) applied GA and simulated annealing for a facility layout problem (FLP). In this paper, we present a genetic algorithm approach to obtain the best layout from the population of the initial layouts of the multi-factor user interface components layout problem for the example task under consideration as given in Peer et al. (2003). Then, the final layouts are compared with that of the improvement procedure for the reported case under consideration. …