research-article

Optimizing hierarchical menus by genetic algorithm and simulated annealing

Authors:

Shouichi Matsui,

Seiji YamadaAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1587 - 1594

https://doi.org/10.1145/1389095.1389397

Published: 12 July 2008 Publication History

Abstract

Hierarchical menus are now ubiquitous. The performance of the menu depends on many factors: structure, layout, colors and so on. There has been extensive research on novel menus, but there has been little work on improving performance by optimizing the menu's structure. This paper proposes algorithms based on the genetic algorithm (GA) and the simulated annealing (SA) for optimizing the performance of menus. The algorithms aim to minimize the average selection time of menu items by considering the user's pointer movement and search/decision time. We will show the experimental results on a static hierarchical menu of a cellular phone as an example where a small screen and limited input device are assumed. We will also show performance comparison of the GA-based algorithm and the SA-based one by using wide varieties of usage patterns.

References

[1]

St. Amant, T.E. Horton, and F.E. Ritter. Model-based evaluation of cell phone menu interaction. In Proc. CHI 2004, pages 343--350 2004.

Digital Library

[2]

D. Ahlström. Modeling and improving selection in cascading pull-down menus using Fitts' law, the steering law and force fields. In Proc. CHI 2005, pages 61--70, 2005.

Digital Library

[3]

J. Beck, S.H. Han, and J. Park. Presenting a submenu window for menu search on a cellular phone. Int. J. of Human-Computer Interaction, 20(3):233--245, 2006.

[4]

R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval, Addison-Wesley,1999.

Digital Library

[5]

A. Cockburn, G. Gutwin, and S. Greenberg A predictive model of menu performance. In Proc. CHI 2007, pages 627--636, 2007.

Digital Library

[6]

G. Francis. Designing multifunction displays: an optimization approach. Int. J. of Cognitive Ergonomics, 4(2):107--124, 2000.

[7]

L. Findlater and J. McGrenere. A comparison of static, adaptive, and adaptable menus. In Proc. CHI 2004, pages 89--96, 2004.

Digital Library

[8]

KDDI: Manual for CASIO W43CA, http://www.au.kddi.com/torisetsu/pdf/w43ca/w43ca_torisetsu.pdf, 2006.

[9]

J. I. Kiger. The depth/breadth trade-off in the design of menu-driven user interfaces. Int. J. Man-Mach. Stud., 20(2):201--213, 1984.

Digital Library

[10]

K. Larson and M. Czerwinski. Web page design: implication of memory, structure and scent for information retrieval. In Proc. CHI 1998, pages 25--32, 1998.

Digital Library

[11]

B. Liu, G. Francis, and G. Salvendy. Applying models of visual search to menu design. Int. J. Human-Computer Studies, 56:307--330, 2002.

[12]

S. Matsui and S. Yamada. Genetic algorithm can optimize hierarchical menus, In Proc. of CHI 2008, 2008 (to appear).

Digital Library

[13]

Z. Michalewicz and D.B. Fogel. How to solve it: modern heuristics, Springer-Verlag, 2000.

Digital Library

[14]

J.C. Quiroz, S.J. Louis, and S. M. Dascalu. Interactive evolution of XUL user interfaces. In Proc. of GECCO 2007, pages 2151--2158, 2007.

Digital Library

[15]

M. Silfverberg, I.S. MacKenzie, and T. Kauppinen. Predicting text entry speed on mobile phones. In Proc. CHI 2000, pages 9--16, 2000.

Digital Library

[16]

E.E. Schultz Jr. and P.S. Curran. Menu structure and ordering of menu selection: independent or interactive effects? SIGCHI Bull., 18(2):69--71, 1986.

Digital Library

[17]

M.L. Toms, M.A. Cummings-Hill, D.G. Curry, and S.M. Cone. Using cluster analysis for deriving menu structures for automotive mobile multimedia applications. SAE Technical Paper Series 2001-01-0359, SAE, 2001.

[18]

P. Zaphiris. Depth vs breadth in the arrangement of web links. In Proc. 44th Annual Meeting of the Human Factors and Ergonomics Society, pages 139--144, 2000.

[19]

P. Zaphiris, S.H. Kurniawan, and R.D. Ellis. Age related difference and the depth vs. breadth tradeoffs in hierarchical online information systems. In Proc. User Interfaces for All, LNCS 2615, pages 23--42, 2003.

Digital Library

[20]

M. Ziefle and S. Bay. Mental models of a cellular phone menu. Comparing older and younger novice users. In Proc. MobileHCI 2004, LNCS 3160, pages 25--37, 2004.

Cited By

Bailly GMalacria S(2022)Command SelectionHandbook of Human Computer Interaction10.1007/978-3-319-27648-9_19-1(1-35)Online publication date: 12-Mar-2022
https://doi.org/10.1007/978-3-319-27648-9_19-1
Chujkova EAidinyan ATsvetkova O(2020)Adaptation Algorithm for Application MenusProgramming and Computer Software10.1134/S036176882006003146:6(397-405)Online publication date: 4-Dec-2020
https://doi.org/10.1134/S0361768820060031
Oulasvirta ADayama NShiripour MJohn MKarrenbauer A(2020)Combinatorial Optimization of Graphical User Interface DesignsProceedings of the IEEE10.1109/JPROC.2020.2969687(1-31)Online publication date: 2020
https://doi.org/10.1109/JPROC.2020.2969687
Show More Cited By

Index Terms

Optimizing hierarchical menus by genetic algorithm and simulated annealing
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Touch screens
    2. Interaction paradigms
  2. Interaction design

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Published In

cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2008

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GECCO08

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Bailly GMalacria S(2022)Command SelectionHandbook of Human Computer Interaction10.1007/978-3-319-27648-9_19-1(1-35)Online publication date: 12-Mar-2022
https://doi.org/10.1007/978-3-319-27648-9_19-1
Chujkova EAidinyan ATsvetkova O(2020)Adaptation Algorithm for Application MenusProgramming and Computer Software10.1134/S036176882006003146:6(397-405)Online publication date: 4-Dec-2020
https://doi.org/10.1134/S0361768820060031
Oulasvirta ADayama NShiripour MJohn MKarrenbauer A(2020)Combinatorial Optimization of Graphical User Interface DesignsProceedings of the IEEE10.1109/JPROC.2020.2969687(1-31)Online publication date: 2020
https://doi.org/10.1109/JPROC.2020.2969687
Lattas ASpinellis DPaulisch FBosch J(2018)Echoes from spaceProceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice10.1145/3183519.3183545(105-114)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3183519.3183545
Hardman NColombi JJacques DHill RMiller J(2009)Application of a seeded hybrid genetic algorithm for user interface designProceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics10.5555/1732323.1732402(462-467)Online publication date: 11-Oct-2009
https://dl.acm.org/doi/10.5555/1732323.1732402
Matsui SYamada S(2009)Performance evaluation of a genetic algorithm for optimizing hierarchical menusProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689724(947-954)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689724
Hardman NColombi JJacques DHill RMiller J(2009)Application of a seeded hybrid genetic algorithm for user interface design2009 IEEE International Conference on Systems, Man and Cybernetics10.1109/ICSMC.2009.5346789(462-467)Online publication date: Oct-2009
https://doi.org/10.1109/ICSMC.2009.5346789

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