EvoFashion: Customising Fashion Through Evolution

  • Nuno Lourenço
  • Filipe Assunção
  • Catarina Maçãs
  • Penousal Machado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10198)


In today’s society, where everyone desires unique and fashionable products, the ability to customise products is almost mandatory in every online store. Despite of many stores allowing the users to personalize their products, they do not always do it in the most efficient and user-friendly manner. In order to have products that reflect the user’s design preferences, they have to go through a laborious process of picking the components that they want to customise. In this paper we propose a framework that aims to relieve the design burden from the user side, by automating the design process through the use of Interactive Evolutionary Computation (IEC). The framework is based on a web-interface that facilitates the interaction between the user and the evolutionary process. The user can select between two types of evolution: (i) automatic; and (ii) partially-automatic. The results show the ability of the framework to promote evolution towards solutions that reflect the user aesthetic preferences.


Evolutionary algorithm Fashion design Interactive evolutionary computation Product customisation 



All shoe images are copyright of MYSWEAR (, and are used as fair use for academic purposes only. We gratefully acknowledge the support of NVIDIA Corporation for the donation of a Titan X GPU. We would also like to thank Tiago Martins for all the patience making the charts herein presented.


  1. 1.
    Pine, B.J.: Markets of One: Creating Customer-Unique Value Through Mass Customization. Harvard Business Press, Brighton (2000)Google Scholar
  2. 2.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, vol. 3. Springer, Heidelberg (2003)CrossRefzbMATHGoogle Scholar
  3. 3.
    Caldas, L.G., Norford, L.K.: A genetic algorithm tool for design optimization. In: Proceedings of the 1999 Conference of the Association for Computer-Aided Design in Architecture (ACADIA 1999), Salt Lake City, UT (1999)Google Scholar
  4. 4.
    Besserud, K., Cotten, J.: Architectural genomics (2008)Google Scholar
  5. 5.
    Michalek, J., Choudhary, R., Papalambros, P.: Architectural layout design optimization. Eng. Optim. 34(5), 461–484 (2002)CrossRefGoogle Scholar
  6. 6.
    Juan, Y.K., Shih, S.G., Perng, Y.H.: Decision support for housing customization: a hybrid approach using case-based reasoning and genetic algorithm. Expert Syst. Appl. 31(1), 83–93 (2006)CrossRefGoogle Scholar
  7. 7.
    Hornby, G.S., Globus, A., Linden, D.S., Lohn, J.D.: Automated antenna design with evolutionary algorithms. In: AIAA Space, pp. 19–21 (2006)Google Scholar
  8. 8.
    Gonzalez-Morcillo, C., Martin, V.J., Vallejo, D., Castro-Schez, J.J., Albusac, J.: Gaudii: an automated graphic design expert system. In: IAAI (2010)Google Scholar
  9. 9.
    O’Donovan, P., Agarwala, A., Hertzmann, A.: Learning layouts for single-pagegraphic designs. IEEE Trans. Visual. Comput. Graphics 20(8), 1200–1213 (2014)CrossRefGoogle Scholar
  10. 10.
    Geigel, J., Loui, A.: Using genetic algorithms for album page layouts. IEEE Multimedia 10(4), 16–27 (2003)CrossRefGoogle Scholar
  11. 11.
    Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. IEEE Proc. 89(9), 1275–1296 (2001)CrossRefGoogle Scholar
  12. 12.
    Kim, H.S., Cho, S.B.: Application of interactive genetic algorithm to fashion design. Eng. Appl. Artif. Intell. 13(6), 635–644 (2000)CrossRefGoogle Scholar
  13. 13.
    Khajeh, M., Payvandy, P., Derakhshan, S.J.: Fashion set design with an emphasis on fabric composition using the interactive genetic algorithm. Fashion Text. 3(1), 1–16 (2016)CrossRefGoogle Scholar
  14. 14.
    Crispin, A., Clay, P., Taylor, G., Bayes, T., Reedman, D.: Genetic algorithm coding methods for leather nesting. Appl. Intell. 23(1), 9–20 (2005)CrossRefGoogle Scholar
  15. 15.
    Shiyou, Y., Guangzheng, N., Yan, L., Renyuan, T.: Shape optimization of pole shoes in harmonic exciting synchronous generators using a stochastic algorithm. IEEE Trans. Magn. 33(2), 1920–1923 (1997)CrossRefGoogle Scholar
  16. 16.
    Shimoyama, K., Seo, K., Nishiwaki, T., Jeong, S., Obayashi, S.: Design optimization of a sport shoe sole structure by evolutionary computation and finite element method analysis. Proc. Inst. Mech. Eng. Part P J. Sports Eng. Technol. 225(4), 179–188 (2011)Google Scholar
  17. 17.
    Dasan, A.: The evolutionary design of generative shoes. (2012). Accessed 06 Nov 2016
  18. 18.
    Machado, P., Romero, J., Cardoso, A., Santos, A.: Partially interactive evolutionary artists. New Gener. Comput. 23(2), 143–155 (2005)CrossRefGoogle Scholar
  19. 19.
    Vinhas, A., Assunção, F., Correia, J., Ekárt, A., Machado, P.: Fitness and novelty in evolutionary art. In: Johnson, C., Ciesielski, V., Correia, J., Machado, P. (eds.) EvoMUSART 2016. LNCS, vol. 9596, pp. 225–240. Springer, Cham (2016). doi: 10.1007/978-3-319-31008-4_16 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nuno Lourenço
    • 1
  • Filipe Assunção
    • 1
  • Catarina Maçãs
    • 1
  • Penousal Machado
    • 1
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

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