We propose a method for automatically optimizing the layout placement of user interfaces in commercial applications. The proposed method suggests an optimal user interface component placement layout for an application by considering behavior cost, repeated user interface sequences, and preferred placement area. We used the Apriori algorithm and a genetic algorithm efficiently to optimize user interface component placement based on the evaluation of a keystroke-level model. We verified the effectiveness of the proposed method using a customizable user interface for three applications, namely, Adobe Photoshop, 3DS MAX, and the massively multiplayer online role-playing game “World of Warcraft.” Our experimental results show that the proposed system can both reduce the behavioral cost of an application at the user level and enable efficient user interface usage by considering interrelationship patterns among user interface components.
El-NasrMSDrachenACanossaA. Game analytics: maximizing the value of player data. Berlin: Springer, 2013.
3.
CardSKMoranTPNewellA. The keystroke-level model for user performance time with interactive systems. Commun ACM1980; 23: 396–410.
4.
BeardDVSmithDKDenelsbeckKM. QGOMS: a direct-manipulation tool for simple GOMS models. In: Conference companion on human factors in computing systems, Vancouver, BC, Canada, 13–18 April 1996, pp. 25–26. New York: ACM.
5.
HolleisPOttoFHussmannH, et al. Keystroke-level model for advanced mobile phone interaction. In: Proceedings of the SIGCHI conference on human factors in computing systems, San Jose, CA, 3 May 2007, pp. 1505–1514. New York: ACM.
6.
JohnBVeraAMatessaM, et al. Automating CPM-GOMS. In: Proceedings of the SIGCHI conference on human factors in computing systems, Minneapolis, MN, 20–25 April 2002, pp. 147–154. New York: ACM.
7.
JohnBEPrevasKSalvucciDD, et al. Predictive human performance modeling made easy. In: Proceedings of the SIGCHI conference on human factors in computing systems, Vienna, 24–29 April 2004, pp. 455–462. New York: ACM.
8.
KarousosNKatsanosCTseliosN, et al. Effortless tool-based evaluation of web form filling tasks using keystroke level model and Fitts law. In: CHI’13 extended abstracts on human factors in computing systems, Paris, 2 May 2013, pp. 1851–1856. New York: ACM.
9.
HudsonSEJohnBEKnudsenK, et al. A tool for creating predictive performance models from user interface demonstrations. In: Proceedings of the 12th annual ACM symposium on user interface software and technology, Asheville, NC, 7–10 November 1999, pp. 93–102. New York: ACM.
SchulzT. Using the keystroke-level model to evaluate mobile phones. In: Proceedings of the 31st information systems research seminar (IRIS), Are, 9–12 August 2008, vol. 31.
12.
PattonEWGrayWD. SANLab-CM: a tool for incorporating stochastic operations into activity network modeling. Behav Res Methods2010; 42: 877–883.
13.
KimHSChoSB. Application of interactive genetic algorithm to fashion design. Eng Appl Artif Intel2000; 13(6): 635–644.
14.
SugaharaMMikiMHiroyasuT. Design of Japanese Kimono using interactive genetic algorithm. In: IEEE international conference on systems, man and cybernetics, Singapore, 12–15 October 2008, pp. 185–190. New York: IEEE.
15.
YoshidaKNakagawaYKoppenM. Interactive genetic algorithm for font generation system. In: World automation congress (WAC), Kobe, 19–23 September 2010, pp. 1–6. New York: IEEE.
16.
BirtoloCPaganoPTroianoL.Evolving colors in user interfaces by interactive genetic algorithm. In: World congress on nature biologically inspired computing, Coimbatore, India, 9–11 December 2009, pp. 349–355. New York: IEEE.
17.
ArakiYOsanaY. Office layout support system for polygonal space using interactive genetic algorithm generation of layout plans for workspace. In: IEEE international conference on systems, man, and cybernetics, Seoul, South Korea, 14–17 October 2012, pp. 1039–1044. New York: IEEE.
18.
QuirozJCLouisSJShankarA, et al. Interactive genetic algorithms for user interface design. In: IEEE congress on evolutionary computation, Singapore, 25–28 September 2007, pp. 1366–1373. New York: IEEE.
19.
MikiMOritaHWakeSH, et al. Design of sign sounds using an interactive genetic algorithm. In: IEEE international conference on systems, man and cybernetics, Taipei, Taiwan, 8–11 October 2006, vol. 4, pp. 3486–3490. New York: IEEE.
20.
QuirozJCLouisSJShankarA, et al. Interactive genetic algorithms for user interface design. In: IEEE congress on evolutionary computation, Singapore, 25–28 September 2007, pp. 1366–1373. New York: IEEE.
21.
SornDRimcharoenS.Web page template design using interactive genetic algorithm. In: International computer science and engineering conference, Nakorn Pathom, Thailand, 4–6 September 2013, pp. 201–206. New York: IEEE.
AdeniyiAMOlalekanAS. An improved genetic algorithm-based test coverage analysis for graphical user interface software. Am J Softw Eng Appl2016; 5(2): 7–14.
25.
SakulinSAlfimtsevASolovyevD, et al. Web page interface optimization based on nature-inspired algorithms. Int J Swarm Intell Res2018; 9(2): 28–46.
26.
AgrawalRSrikantR. Fast algorithms for mining association rules. In: Proceedings of 20th international conference on very large databases, Santiago de Chile, 12–15 September 1994, pp. 487–499. San Francisco, CA: Morgan Kaufmann Publishers Inc.
MitchellM. An introduction to genetic algorithms. Cambridge, MA: The MIT Press, 1998.
29.
OlivierISmithDHollandJ. A study of permutation crossover operators on the traveling salesman problem. In: Proceedings of second international conference on genetic algorithms, Cambridge, MA, 28–31 July 1987, pp. 224–230. Hillsdale, NJ: L. Erlbaum Associates Inc.
30.
GoldbergDELingleR. Alleles, loci, and the traveling salesman problem. In: Proceedings of international conference on genetic algorithms and their applications, Los Angeles, CA, 24–26 July 1985, pp. 154–159. Hillsdale, NJ: L. Erlbaum Associates Inc.
31.
WhitleyLDStarkweatherTFuquayDA. Scheduling problems and traveling salesmen: the genetic edge recombination operator. In: Proceedings of the 3rd international conference on genetic algorithms (ICGA), Fairfax, VA, 4–7 June 1989, pp. 133–140. San Francisco, CA: Morgan Kaufmann Publishers Inc.
32.
LouisSJRawlinsGJ.Designer genetic algorithms: genetic algorithms in structure design. In: Proceedings of the fourth international conference on genetic algorithms (ICGA), San Diego, CA, 13–16 July 1991, pp. 53–60. San Francisco, CA: Morgan Kaufmann Publishers Inc.
33.
BansalNSviridenkoM. New approximability and inapproximability results for 2-dimensional bin packing. In: SODA ‘04: Proceedings of the 15th annual ACM–SIAM symposium on discrete algorithms, New Orleans, LA, 11–14 January 2004, pp. 196–203. Philadelphia, PA: Society for Industrial and Applied Mathematics.
