Application of Artificial Intelligence for Weekly Dietary Menu Planning

  • Balázs Gaál
  • István Vassányi
  • György Kozmann
Part of the Studies in Computational Intelligence book series (SCI, volume 65)


Dietary menu planning is an important part of personalized lifestyle counseling. The chapter describes the results of an automated menu generator (MenuGene) of the web-based lifestyle counseling system Cordelia that provides personalized advice to prevent cardiovascular diseases. The menu generator uses Genetic Algorithms to prepare weekly menus for web users. The objectives are derived from personal medical data collected via forms, combined with general nutritional guidelines. The weekly menu is modeled as a multi-level structure. Results show that the Genetic Algorithm based method succeeds in planning dietary menus that satisfy strict numerical constraints on every nutritional level (meal, daily basis, weekly basis). The rule-based assessment proved capable of manipulating the mean occurrence of the nutritional components thus providing a method for adjusting the variety and harmony of the menu plans. By splitting the problem into well determined subproblems, weekly menu plans that satisfy nutritional constraints and have well assorted components can be generated with the same method that is used for daily and meal plan generation.


Nutrition Counseling Meal Plan Numerical Constraint Daily Plan Nutritional Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    The interactive menu planner of the National Heart, Lung, and Blood Institute at [Verified June 2006]
  2. 2.
    The Cordelia Dietary and Lifestyle counseling project at [Verified June 2006]
  3. 3.
    Balintfy, J. L.: Menu Planning by Computer, Communications of the ACM, vol. 7, no. 4, pp. 255-259., April, 1964CrossRefGoogle Scholar
  4. 4.
    Dollahite J, Franklin D, McNew R. Problems encountered in meeting the Rec-ommended Dietary Allowances for menus designed according to the Dietary Guidelines for Americans. J Am Diet Assoc. 1995 Mar;95(3):341-4, 347; quiz 345-6CrossRefGoogle Scholar
  5. 5.
    Food and Nutrition Board (FNB), Institute of Medicine (IOM): Dietary Ref-erence Intakes: Applications in Dietary Planning, National Academy Press. Washington, DC. 2003Google Scholar
  6. 6.
    Food and Nutrition Board (FNB), Institute of Medicine (IOM): Dietary Ref-erence Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Choles-terol, Protein, and Amino Acids (Macronutrients), National Academy Press. Washington, DC. 2002Google Scholar
  7. 7.
    Eckstein EF. Menu planning by computer: the random approach. J Am Diet Assoc 1967 Dec;51(6):529-533Google Scholar
  8. 8.
    Hinrichs, R. R. Problem Solving in Open Worlds: A Case Study in Design. Erlbaum, Northvale, NJ. 1992Google Scholar
  9. 9.
    C.R. Marling, G.J. Petot, L.S. Sterling Integrating Case-Based and Rule-Based Reasoning to Meet Multiple Design ConstraintsGoogle Scholar
  10. 10.
    Petot G.J., Marling C.R. and Sterling L. An artificial intelligence system for computer-assisted menu planning. Journal of the American Dietetic Association; 98: 1009-1014, 1998CrossRefGoogle Scholar
  11. 11.
    Kovacic KJ. Using common-sense knowledge for computer menu planning [PhD dissertation]. Cleveland, Ohio: Case Western Reserve University; 1995Google Scholar
  12. 12.
    Khan AS, Hoffmann A. An advanced artificial intelligence tool for menu design. Nutr Health. 2003;17(1):43-53Google Scholar
  13. 13.
    Khan AS, Hoffmann A. Building a case-based diet recommendation system with-out a knowledge engineer. Artif Intell Med. 2003 Feb;27(2):155-79CrossRefGoogle Scholar
  14. 14.
    Noah S, Abdullah S, Shahar S, Abdul-Hamid H, Khairudin N, Yusoff M, Ghazali R, Mohd-Yusoff ., Shafii N, Abdul-Manaf Z DietPal: A Web-Based Dietary Menu-Generating and Management System Journal of Medical Inter-net Research 2004;6(1):e4CrossRefGoogle Scholar
  15. 15.
    Bucolo M, Fortuna L, Frasca M, La Rosa M, Virzi MC, Shannahoff-Khalsa D. A nonlinear circuit architecture for magnetoencephalographic signal analysis. Methods Inf Med. 2004;43(1):89-93Google Scholar
  16. 16.
    Laurikkala J, Juhola M, Lammi S, Viikki K. Comparison of genetic algorithms and other classification methods in the diagnosis of female urinary inconti-nence. Methods Inf Med. 1999 Jun;38(2):125-31Google Scholar
  17. 17.
    Carlos Andrés Pena-Reyes, Moshe Sipper Evolutionary computation in medi-cine: an overview, Artificial Intelligence in Medicine 19 (2000) 1-23Google Scholar
  18. 18.
    P.S. Heckerling, B.S. Gerber, T.G. Tape, R.S. Wigton Selection of Predictor Variables for Pneumonia Using Neural Networks and Genetic Algorithms, Meth-ods Inf Med 2005; 44: 89-97Google Scholar
  19. 19.
    Coello Coello, C.A.: A comprehensive survey of evolutionary-based multi-objective optimization techniques, Int. J. Knowledge Inform. Syst 1, 269-309, 1999Google Scholar
  20. 20.
    Multi-level Multi-objective Genetic Algorithm Using Entropy to Preserve Diver-sity, EMO 2003, LNCS 2632, pp. 148-161, 2003Google Scholar
  21. 21.
    The M.I.T. GALib C + + Library of Genetic Algorithm Components at [Verified June 2006]

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Balázs Gaál
    • 1
  • István Vassányi
    • 2
  • György Kozmann
    • 3
  1. 1.Department of Information SystemsUniversity of PannoniaVeszprémHungary
  2. 2.Department of Information SystemsUniversity of PannoniaVeszprémHungary
  3. 3.Department of Information SystemsUniversity of PannoniaVeszprémHungary

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