Advertisement

Searching optimal menu layouts by linear genetic programming

  • Luigi TroianoEmail author
  • Cosimo Birtolo
  • Roberto Armenise
Original Research

Abstract

Designing effective menu systems is a key ingredient to usable graphical user interfaces. This task generally relies only on human ability in building hierarchical structures. However, trading off different and partially opposite guidelines, standards and practices is time consuming and can exceed human skills in problem solving. Recent advances are showing that this task can be addressed by generative approaches which exploit evolutionary algorithms as means for evolving different and unexpected solutions. The search of optimal solutions is made not trivial due to different alternatives which lead to local optima and constraints which can invalidate large sectors of the search space and make valid solutions sparse. This problem can be addressed by choosing an appropriate algorithm. In this paper we face the problem of searching optimal solutions by Linear Genetic Programming in particular, and we compare the solution to more conventional approaches based on simple genetic algorithms and genetic programming. Experimental results are discussed and compared to human-made solutions.

Keywords

Genetic algorithm Genetic programming Menu layout Search based software engineering 

References

  1. Accot J, Zhai S (1997) Beyond fitts’ law: models for trajectory-based hci tasks. In: CHI ’97: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, New York, NY, USA, pp 295–302. doi: 10.1145/258549.258760
  2. Ahlström D (2005) Modeling and improving selection in cascading pull-down menus using fitts’ law, the steering law and force fields. In: CHI ’05: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, New York, NY, USA, pp 61–70. doi: 10.1145/1054972.1054982
  3. Ahlström D, Alexandrowicz R, Hitz M (2006) Improving menu interaction: a comparison of standard, force enhanced and jumping menus. In: CHI ’06: Proceedings of the SIGCHI conference on Human Factors in computing systems, ACM, New York, NY, USA, pp 1067–1076. doi: 10.1145/1124772.1124932
  4. Amant RS, Horton TE, Ritter FE (2007) Model-based evaluation of expert cell phone menu interaction. ACM Trans Comput-Hum Interact 14(1):1. doi: 10.1145/1229855.1229856 CrossRefGoogle Scholar
  5. Apple Computer Inc (2006) Apple human interface guidelines. In: Tech. rep., Apple Computer IncGoogle Scholar
  6. Bernard ML (2002) Examining a metric for predicting the accessibility of information within hypertext structures. Ph.D. thesis, Wichita, KS, USA, adviser-Charles G. HalcombGoogle Scholar
  7. Birtolo C, Armenise R, Troiano L (2010) Supporting menu layout design by genetic programming. In: Filipe J, Cordeiro J (eds) ICEIS 2010–proceedings of the 12th international conference on enterprise information systems, HCI, Funchal, Madeira, Portugal, June 8–12, 2010, vol 5. SciTePress, pp 248–251Google Scholar
  8. Botafogo RA, Rivlin E, Shneiderman B (1992) Structural analysis of hypertexts: identifying hierarchies and useful metrics. ACM Trans Inf Syst 10(2):142–180. doi: 10.1145/146802.146826 CrossRefGoogle Scholar
  9. Brameier M, Banzhaf W (2001) A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans Evolut Comput 5(1):17–26. doi: 10.1109/4235.910462 CrossRefzbMATHGoogle Scholar
  10. Brameier MF, Banzhaf W (2010) Linear genetic programming, 1st edn. Springer, BerlinzbMATHGoogle Scholar
  11. Cockburn A, Gutwin C, Greenberg S (2007) A predictive model of menu performance. In: CHI ’07: proceedings of the SIGCHI conference on Human factors in computing systems, ACM, New York, NY, USA, pp 627–636. doi: 10.1145/1240624.1240723
  12. Downey C, Zhang M, Browne WN (2010) New crossover operators in linear genetic programming for multiclass object classification. In: Proceedings of the 12th annual conference on Genetic and evolutionary computation, ACM, New York, NY, USA, GECCO ’10, pp 885–892. doi: 10.1145/1830483.1830644
  13. du Plessis MC, Barnard L (2008) Incorporating layout managers into an evolutionary programming algorithm to design graphical user interfaces. In: Proceedings of the 2008 annual research cONFERENCE of the South African institute of computer scientists and information technologists on IT research in developing countries: riding the wave of technology, ACM, New York, NY, USA, SAICSIT ’08, pp 41–47. doi: 10.1145/1456659.1456665
  14. Fagan D, Nicolau M, Hemberg E, O’Neill M, Brabazon A, McGarraghy S (2011) Investigation of the performance of different mapping orders for ge on the max problem. Proceedings of the 14th European conference on Genetic programming, EuroGP’11. Springer, Berlin, pp 286–297Google Scholar
  15. Findlater L, McGrenere J (2004) A comparison of static, adaptive, and adaptable menus. In: CHI ’04: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, New York, NY, USA, pp 89–96. doi: 10.1145/985692.985704
  16. Fitts PM (1954) The information capacity of the human motor system in controlling the amplitude of movement. J Exp Psychol 47(6):381–391. http://view.ncbi.nlm.nih.gov/pubmed/13174710
  17. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, BostonzbMATHGoogle Scholar
  18. Hardman N, Colombi J, Jacques D, Hill R, Miller J (2009) Application of a seeded hybrid genetic algorithm for user interface design. In: Proceedings of the 2009 IEEE international conference on systems, man and cybernetics, IEEE Press, Piscataway, NJ, USA, SMC’09, pp 462–467. http://dl.acm.org/citation.cfm?id=1732323.1732402
  19. Hick WE (1952) On the rate of gain of information. Q J Exp Psychol 4:11–26CrossRefGoogle Scholar
  20. Hollink V, Someren M, Wielinga BJ (2007) Navigation behavior models for link structure optimization. User Model User-Adapt Interact 17(4):339–377. doi: 10.1007/s11257-007-9030-0 CrossRefGoogle Scholar
  21. Hollink V, van Someren M (2006) Validating navigation time prediction models for menu optimization. In: Althoff KD, Schaaf M (eds) LWA, University of Hildesheim, Institute of Computer Science, Hildesheimer Informatik-Berichte, vol 1/2006, pp 47–52Google Scholar
  22. Hu T, Banzhaf W (2009) Neutrality and variability: two sides of evolvability in linear genetic programming. In: Proceedings of the 11th annual conference on Genetic and evolutionary computation, ACM, New York, NY, USA, GECCO ’09, pp 963–970. doi: 10.1145/1569901.1570033
  23. Humayoun SR, AlTarawneh R, Ebert A, Dubinsky Y (2014) Automate the decision on best-suited ui design for mobile apps. In: Proceedings of the 1st international conference on mobile software engineering and systems, ACM, New York, NY, USA, MOBILESoft 2014, pp 66–68. doi: 10.1145/2593902.2593919
  24. Inc SM (2001) Java look and feel design guidelines: advanced topics. Addison-Wesley, BostonGoogle Scholar
  25. Kong J, Zhang WY, Yu N, Xia XJ (2011) Design of human-centric adaptive multimodal interfaces. Int J Hum-Comput Stud 69(12):854–869CrossRefGoogle Scholar
  26. Masson D, Demeure A, Calvary G (2011) Examples galleries generated by interactive genetic algorithms. In: Procedings of the second conference on creativity and innovation in design, ACM, New York, NY, USA, DESIRE ’11, pp 61–71. doi: 10.1145/2079216.2079225
  27. McGrenere J, Baecker RM, Booth KS (2002) An evaluation of a multiple interface design solution for bloated software. In: CHI ’02: proceedings of the SIGCHI conference on Human factors in computing systems, ACM, New York, NY, USA, pp 164–170. doi: 10.1145/503376.503406
  28. Norman KL (1991) The psychology of menu selection: designing cognitive control at the human/computer interface. Greenwood Publishing Group Inc., WestportGoogle Scholar
  29. Oliver A, Regragui O, MonmarchT N, Venturini G (2002) Genetic and interactive optimization of web sites. The 11th international World wide web conference, Honolulu, Hawaii, USA, pp 7–11Google Scholar
  30. Quiroz JC, Louis SJ, Dascalu SM (2007) Interactive evolution of xul user interfaces. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, ACM, New York, NY, USA, GECCO ’07, pp 2151–2158. doi: 10.1145/1276958.1277373
  31. Russo G, Birtolo C, Troiano L (2008) Generative ui design in sapi project. In: CHI ’08 extended abstracts on human factors in computing systems, ACM, New York, NY, USA, CHI EA ’08, pp 3627–3632. doi: 10.1145/1358628.1358903
  32. Singh N, Bhattacharya S (2010) A ga-based approach to improve web page aesthetics. In: Proceedings of the first international conference on intelligent interactive technologies and multimedia, ACM, New York, NY, USA, IITM ’10, pp 29–32. doi: 10.1145/1963564.1963569
  33. Troiano L, Rodríguez-Muñiz L, Díaz I (2014) Discovering user preferences using dempster-shafer theory. Fuzzy Sets Syst. doi: 10.1016/j.fss.2015.06.004 Google Scholar
  34. Troiano L, Birtolo C (2014) Genetic algorithms supporting generative design of user interfaces: examples. Inf Sci 259:433–451. doi: 10.1016/j.ins.2012.01.006 CrossRefGoogle Scholar
  35. Troiano L, Birtolo C, Miranda M (2008) Adapting palettes to color vision deficiencies by genetic algorithm. In: Proceedings of the 10th annual conference on genetic and evolutionary computation, ACM, New York, NY, USA, GECCO ’08, pp 1065–1072. doi: 10.1145/1389095.1389291
  36. Troiano L, Scibelli G (2014a) Mining frequent itemsets in data streams within a time horizon. Data Knowl Eng 89:21–37. doi: 10.1016/j.datak.2013.10.002 CrossRefzbMATHGoogle Scholar
  37. Troiano L, Scibelli G (2014b) A time-efficient breadth-first level-wise lattice-traversal algorithm to discover rare itemsets. Data Min Knowl Discov 28(3):773–807. doi: 10.1007/s10618-013-0304-3 MathSciNetCrossRefzbMATHGoogle Scholar
  38. Tsandilas T, Schraefel MC (2007) Bubbling menus: a selective mechanism for accessing hierarchical drop-down menus. In: CHI ’07: proceedings of the SIGCHI conference on Human factors in computing systems, ACM, New York, NY, USA, pp 1195–1204. doi: 10.1145/1240624.1240806
  39. Walker N, Smelcer JB (1990) A comparison of selection times from walking and pull-down menus. In: Proceedings of ACM CHI 1990 conference on human factors in computing systems, pp 221–225Google Scholar
  40. Wilson G, Leblanc D, Banzhaf W (2011) Stock trading using linear genetic programming with multiple time frames. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation, ACM, New York, NY, USA, GECCO ’11, pp 1667–1674. doi: 10.1145/2001576.2001801

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Luigi Troiano
    • 1
    Email author
  • Cosimo Birtolo
    • 2
  • Roberto Armenise
    • 2
  1. 1.Department of EngineeringUniversity of SannioBeneventoItaly
  2. 2.Poste Italiane S.p.A., SI/CCUP/GCNapoliItaly

Personalised recommendations