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Designing a Website Using a Genetic Algorithm

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Artificial Intelligence XXXV (SGAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11311))

Abstract

This paper describes the use of a genetic algorithm to design a website, according to principles of clarity, symmetry, golden ratio and image size. The website’s logo is used to calculate a matching colour scheme. Results indicate that local maxima can be a problem but that with the right weighting of the fitness function, a pleasing design can be achieved.

Such a program could be used when designing large numbers of websites; when a website has to be re-designed regularly to match changing content; or to provide a starting point for human website designers or users of interactive genetic algorithms to improve.

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References

  1. Lavie, T., Tractinsky, N.: Assessing dimensions of perceived visual aesthetics of websites. Int. J. Hum. Comput. Stud. 60(3), 269–298 (2004)

    Article  Google Scholar 

  2. Oyibo, K., Vassileva, J.: What drives perceived usability in mobile web design: classical or expressive aesthetics? In: Marcus, A., Wang, W. (eds.) DUXU 2017. LNCS, vol. 10288, pp. 445–462. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58634-2_33

    Chapter  Google Scholar 

  3. Peracchio, L.A., Luna, D.: The role of thin-slice judgments in consumer psychology. J. Consum. Psychol. 16, 25–32 (2006)

    Article  Google Scholar 

  4. Thompson, C.: Search engines invite new problems. Market. Manag. 13, 52–53 (2004)

    Google Scholar 

  5. Lowry, P.B., Wilson, D.W., Haig, W.L.: A picture is worth a thousand words: source credibility theory applied to logo and website design for heightened credibility and consumer trust. Int. J. Hum. Comput. Interact. 30(1), 63–93 (2014)

    Article  Google Scholar 

  6. Park, S.: Webpage design optimuization using genetic algorithm driven CSS. Retrospective Theses and Dissertations, 14548, Iowa State University (2007). https://lib.dr.iastate.edu/14548

  7. Mensch, E.P.: Optimizing Website Design Through the Application of an Interactive Genetic Algorithm. Senior Projects Spring (2016). http://digitalcommons.bard.edu/senproj_s2016/313

  8. Olliver, A., Monmarchё, N., Venturini, G.: Interactiove design of web sites with a genetic algorithm. In: Proceedings of tne IADIS Internatiinal Conference WWW/Internet, pp. 355–362 (2002)

    Google Scholar 

  9. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, London (1996)

    MATH  Google Scholar 

  10. Kora, P., Yadlapalli, P.: Crossover operators in genetic algorithms: a review. Int. J. Comput. Appl. 162(10), 34–36 (2017)

    Google Scholar 

  11. Chawdhry, P.K., Roy, R., Pant, R.K. (eds.): Soft Computing in Engineering Design and Manufacturing. Springer, London (1998). https://doi.org/10.1007/978-1-4471-0427-8

    Book  Google Scholar 

  12. Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution strategies, Evolutionary programming, Genetic Algorithms. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  13. Baluja, S., Caruana, R.: Removing the genetics from the standard genetic algorithm. In: Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, CA (1995)

    Google Scholar 

  14. Ahn, C.W., Ramakrishna, R.S.: Elitism-based compact genetic algorithms. IEEE Trans. Evol. Comput. 7(4), 367–385 (2003)

    Article  Google Scholar 

  15. Hussam, A.: The gestalt principle: design theory for web designers. Web Design Theory Blog, 12 January 2011. https://webdesign.tutsplus.com/articles/the-gestalt-principle-design-theory-for-web-designers–webdesign-1756

  16. Arnheim, R.: Kunst und Sehen: Eine Psychologie des schöpferischen Auges, 3rd edn. De Gruyter, Berlin (2000)

    Book  Google Scholar 

  17. Tuch, A.N., Bargas-Avila, J.A., Opwis, K.: Symmetry and aesthetics in website design: it’s a man’s business. Comput. Hum. Behav. 26(6), 1831–1837 (2010)

    Article  Google Scholar 

  18. Altaboli, A., Lin, Y.: Effects of unity of form and symmetry on visual aesthetics of website interface design. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 56(1), 728–732 (2012)

    Article  Google Scholar 

  19. Hemenway, P., Ray, A.: Divine Proportion: Phi in Art, Nature, and Science. Sterling, New York (2005)

    Google Scholar 

  20. Lieberman, M.: How important are images in your website design? Inbound – the Blog, 22 March 2012. https://www.square2marketing.com/blog/bid/121405/how-important-are-images-in-your-website-design

  21. Djamasbi, S., Siegel, M., Tullis, T., Generation, Y.: Web design, and eye tracking. Int. J. Hum. Comput. Stud. 68(5), 307–323 (2010)

    Article  Google Scholar 

  22. Tullis, T.S., Tullis, C.M.: Statistical analyses of E-commerce websites: can a site be usable and beautiful? In: 12th International Conference on Human-Computer Interaction, Beijing, China (2007)

    Google Scholar 

  23. Jones, B.: Color theory: the importance of color in Web design. Design and Promote Blog, 27 June 2014. https://www.designandpromote.com/color-theory-the-importance-of-color-in-web-design/

  24. Alabsi, F., Naoum, R.: Comparison of selection methods and crossover operations using steady state genetic based intrusion detection system. J. Emerg. Trends Comput. Inf. Sci. 3(7), 1053–1058 (2012)

    Google Scholar 

  25. Takagi, H.: Interactive evolutionary computation: fusion of the capacities of EC optimization and human evaluation. Proc. IEEE 89(9), 1275–1296 (2000)

    Article  Google Scholar 

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Acknowledgements

The colour wheel image (Fig. 1) is from https://www.sessions.edu/color-calculator/. The GALGO algorithm was obtained from https://github.com/olmallet81/GALGO-2.0. CImg was obtained from http://cimg.eu/.

Author information

Authors and Affiliations

  1. School of Engineering and Informatics, University of Sussex, Brighton, UK

    Lukas Günthermann

  2. Centre for Secure, Intelligent and Usable Systems, University of Brighton, Brighton, UK

    John Kingston

Authors
  1. Lukas Günthermann
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  2. John Kingston
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Corresponding author

Correspondence to John Kingston .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. Middlesex University, London, UK

    Miltos Petridis

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Günthermann, L., Kingston, J. (2018). Designing a Website Using a Genetic Algorithm. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_32

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  • DOI: https://doi.org/10.1007/978-3-030-04191-5_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04190-8

  • Online ISBN: 978-3-030-04191-5

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