Emojinating: Evolving Emoji Blends

  • João M. CunhaEmail author
  • Nuno Lourenço
  • João Correia
  • Pedro Martins
  • Penousal Machado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11453)


Graphic designers visually represent concepts in several of their daily tasks, such as in icon design. Computational systems can be of help in such tasks by stimulating creativity. However, current computational approaches to concept visual representation lack in effectiveness in promoting the exploration of the space of possible solutions. In this paper, we present an evolutionary approach that combines a standard Evolutionary Algorithm with a method inspired by Estimation of Distribution Algorithms to evolve emoji blends to represent user-introduced concepts. The quality of the developed approach is assessed using two separate user-studies. In comparison to previous approaches, our evolutionary system is able to better explore the search space, obtaining solutions of higher quality in terms of concept representativeness.


Evolutionary Algorithm Emoji Interactive Evolutionary Computation Visual blending 



João M. Cunha is partially funded by Fundação para a Ciência e Tecnologia (FCT), Portugal, under the grant SFRH/BD/120905/2016; and is based upon work from COST Action CA15140: ImAppNIO, supported by COST (European Cooperation in Science and Technology): This work includes data from ConceptNet 5, which was compiled by the Commonsense Computing Initiative and is freely available under the Creative Commons Attribution-ShareAlike license (CC BY SA 4.0) from


  1. 1.
    McCorduck, P.: Aaron’s code: meta-art, artificial intelligence, and the work of Harold Cohen. Macmillan (1991)Google Scholar
  2. 2.
    Lee, Y.J., Zitnick, C.L., Cohen, M.F.: Shadowdraw: real-time user guidance for freehand drawing. ACM Trans. Graph. (TOG) 30, 27 (2011)Google Scholar
  3. 3.
    Davis, N., Hsiao, C.P., Singh, K.Y., Magerko, B.: Co-creative drawing agent with object recognition. In: AIIDE 2016 (2016)Google Scholar
  4. 4.
    Parmee, I.C., Abraham, J.A., Machwe, A.: User-centric evolutionary computing: melding human and machine capability to satisfy multiple criteria. In: Knowles, J., Corne, D., Deb, K., Chair, D.R. (eds.) Multiobjective Problem Solving from Nature. Natural Computing Series, pp. 263–283. Springer, Heidelberg (2008). Scholar
  5. 5.
    Cunha, J.M., Martins, P., Machado, P.: How shell and horn make a unicorn: experimenting with visual blending in emoji. In: Proceedings of the Ninth International Conference on Computational Creativity (2018)Google Scholar
  6. 6.
    Cunha, J.M., Martins, P., Machado, P.: Emojinating: representing concepts using emoji. In: Workshop Proceedings from ICCBR 2018 (2018)Google Scholar
  7. 7.
    Wicke, P.: Ideograms as semantic primes: emoji in computational linguistic creativity (2017)Google Scholar
  8. 8.
    Ha, D., Eck, D.: A neural representation of sketch drawings. arXiv preprint arXiv:1704.03477 (2017)
  9. 9.
    Karimi, P., Maher, M.L., Grace, K., Davis, N.: A computational model for visual conceptual blends. IBM J. Res. Dev. (2018)Google Scholar
  10. 10.
    Xiao, P., Linkola, S.: Vismantic: meaning-making with images. In: Proceedings of the Sixth International Conference on Computational Creativity (2015)Google Scholar
  11. 11.
    Correia, J., Martins, T., Martins, P., Machado, P.: X-faces: the exploit is out there. In: Proceedings of the Seventh International Conference on Computational Creativity (2016)Google Scholar
  12. 12.
    Pereira, F.C., Cardoso, A.: The boat-house visual blending experience. In: Proceedings of the Symposium for Creativity in Arts and Science of AISB 2002 (2002)Google Scholar
  13. 13.
    Cunha, J.M., Gonçalves, J., Martins, P., Machado, P., Cardoso, A.: A pig, an angel and a cactus walk into a blender: a descriptive approach to visual blending. In: Proceedings of the Eighth International Conference on Computational Creativity (2017)Google Scholar
  14. 14.
    Lourenço, N., Assunção, F., Maçãs, C., Machado, P.: EvoFashion: customising fashion through evolution. In: Correia, J., Ciesielski, V., Liapis, A. (eds.) EvoMUSART 2017. LNCS, vol. 10198, pp. 176–189. Springer, Cham (2017). Scholar
  15. 15.
    Rebelo, S., Fonseca, C.M.: Experiments in the development of typographical posters. In: 6th Conference on Computation, Communication, Aesthetics and X (2018)Google Scholar
  16. 16.
    Maçãs, C., Lourenço, N., Machado, P.: Interactive evolution of swarms for the visualisation of consumptions. In: ArtsIT 2018 (2018)Google Scholar
  17. 17.
    Dorris, N., Carnahan, B., Orsini, L., Kuntz, L.A.: Interactive evolutionary design of anthropomorphic symbols. In: Congress on Evolutionary Computation, CEC 2004, vol. 1, pp. 433–440. IEEE (2004)Google Scholar
  18. 18.
    Dozier, G., Carnahan, B., Seals, C., Kuntz, L.A., Fu, S.G.: An interactive distributed evolutionary algorithm (idea) for design. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 418–422. IEEE (2005)Google Scholar
  19. 19.
    Piper, A.K.: Participatory design of warning symbols using distributed interactive evolutionary computation. Ph.D. thesis, Auburn University (2010)Google Scholar
  20. 20.
    Hiroyasu, T., Tanaka, M., Ito, F., Miki, M.: Discussion of a crossover method using a probabilistic model for interactive genetic algorithm. In: SCIS & ISIS SCIS & ISIS 2008. Japan Society for Fuzzy Theory and Intelligent Informatics (2008)Google Scholar
  21. 21.
    Gong, D., Yan, J., Zuo, G.: A review of gait optimization based on evolutionary computation. Appl. Comput. Intell. Soft Comput. 2010, 12 (2010)CrossRefGoogle Scholar
  22. 22.
    Pelikan, M., Goldberg, D.E., Lobo, F.G.: A survey of optimization by building and using probabilistic models. Comput. Optim. Appl. 21, 5–20 (2002)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Wijeratne, S., Balasuriya, L., Sheth, A., Doran, D.: Emojinet: an open service and API for emoji sense discovery. In: Proceedings of ICWSM-17 (2017)Google Scholar
  24. 24.
    Speer, R., Havasi, C.: Representing general relational knowledge in conceptnet 5. In: LREC, pp. 3679–3686 (2012)Google Scholar
  25. 25.
    Browne, C.: A new general service list: the better mousetrap we’ve been looking for. Vocabulary Learn. Instr. 3(1), 1–10 (2014)Google Scholar
  26. 26.
    Liapis, A., Yannakakis, G.N., Togelius, J.: Sentient sketchbook: computer-aided game level authoring. In: FDG, pp. 213–220 (2013)Google Scholar
  27. 27.
    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). Scholar
  28. 28.
    Brysbaert, M., Warriner, A.B., Kuperman, V.: Concreteness ratings for 40 thousand generally known english word lemmas. Behav. Res. Methods 46(3), 904–911 (2014)CrossRefGoogle Scholar
  29. 29.
    Cunha, J.M., Martins, P., Machado, P.: Using image schemas in the visual representation of concepts. In: Proceedings of TriCoLore 2018. CEUR (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

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