EvoChef: Show Me What to Cook! Artificial Evolution of Culinary Arts

  • Hajira JabeenEmail author
  • Nargis Tahara
  • Jens Lehmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11453)


Computational Intelligence (CI) has proven its artistry in creation of music, graphics, and drawings. EvoChef demonstrates the creativity of CI in artificial evolution of culinary arts. EvoChef takes input from well-rated recipes of different cuisines and evolves new recipes by recombining the instructions, spices, and ingredients. Each recipe is represented as a property graph containing ingredients, their status, spices, and cooking instructions. These recipes are evolved using recombination and mutation operators. The expert opinion (user ratings) has been used as the fitness function for the evolved recipes. It was observed that the overall fitness of the recipes improved with the number of generations and almost all the resulting recipes were found to be conceptually correct. We also conducted a blind-comparison of the original recipes with the EvoChef recipes and the EvoChef was rated to be more innovative. To the best of our knowledge, EvoChef is the first semi-automated, open source, and valid recipe generator that creates easy to follow, and novel recipes.


Recipe Evolutionary algorithms Culinary art Genetic Algorithm 



This work is partly supported by the EU Horizon2020 projects BigDataOcean (GA no. 732310), LAMBDA (GA no. 809965) and Boost4.0 (GA no. 780732).


  1. 1.
    Kim, K., Chung, C.: Tell me what you eat, and i will tell you where you come from: a data science approach for global recipe data on the web. IEEE Access 4, 8199–8211 (2016)CrossRefGoogle Scholar
  2. 2.
    De Prisco, R., Zaccagnino, R.: An evolutionary music composer algorithm for bass harmonization. In: Giacobini, M., et al. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 567–572. Springer, Heidelberg (2009). Scholar
  3. 3.
    Scirea, M., Togelius, J., Eklund, P., Risi, S.: Affective evolutionary music composition with metacompose. Genet. Program. Evolvable Mach. 18, 1–33 (2017)CrossRefGoogle Scholar
  4. 4.
    Misztal, J., Indurkhya, B.: A computational approach to re-interpretation: generation of emphatic poems inspired by internet blogs (2014)Google Scholar
  5. 5.
    Lewis, M.: Evolutionary visual art and design. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution, pp. 3–37. Springer, Heidelberg (2008). Scholar
  6. 6.
  7. 7.
    Pinel, F.: What’s cooking with chef watson? An interview with lav varshney and james briscione. IEEE Pervasive Comput. 14(4), 58–62 (2015)CrossRefGoogle Scholar
  8. 8.
  9. 9.
  10. 10.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)zbMATHGoogle Scholar
  11. 11.
    Anderson-Cook, C.M.: Practical Genetic Algorithms (2005)Google Scholar
  12. 12.
    Xin, R.S., Gonzalez, J.E., Franklin, M.J., Stoica, I.: GraphX: a resilient distributed graph system on spark. In: First International Workshop on Graph Data Management Experiences and Systems, GRADES 2013, pp. 2:1–2:6. ACM, New York (2013)Google Scholar
  13. 13.
  14. 14.
    Zaharia, M., et al.: Apache Spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)CrossRefGoogle Scholar
  15. 15.
  16. 16.
    Bhatia, A.: A new kind of food science: how IBM is using big data to invent creative recipes (2013). Accessed 01 Mar 2018
  17. 17.
    Cromwell, E., Galeota-Sprung, J., Ramanujan, R.: Computational creativity in the culinary arts (2015)Google Scholar
  18. 18.
  19. 19.
  20. 20.
  21. 21.
    Genius Kitchen - Recipes, Food Ideas And Videos.
  22. 22.
  23. 23.
    Omnivore’s Cookbook.
  24. 24.
  25. 25.
  26. 26.
    MizzNezz: Mashed red potatoes with garlic and parmesan. Accessed Feb 2018
  27. 27.
    Common Ingredient Substitutions (Infographic).
  28. 28.
  29. 29.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Informatik IIIUniversity of BonnBonnGermany
  2. 2.Fraunhofer IAISSankt AugustinGermany

Personalised recommendations