Advertisement

Consecutive Meals Planning by Using Permutation GA: Evaluation Function Proposal for Measuring Appearance Order of Meal’s Characteristics

  • Tomoko KashimaEmail author
  • Yukiko Orito
  • Hiroshi Someya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)

Abstract

The consecutive meals planning is a combinatorial optimization problem that determines a meals plan in one period consisting of consecutive days. This paper proposes an evaluation function using a moving entropy for this problem. The function measures the appearance order of meal’s characteristics on the plan. In the numerical experiments, we apply a permutation GA to the problem. We show that our meals plan is a good solution with large variation of appearance order of meal’s characteristics for the consecutive meals planning.

Notes

Acknowledgements

This work was supported by JSPS KAKENHI Grant Numbers #25750007 and #15K00339.

References

  1. 1.
    Lancaster, L.M.: The history of the application of mathematical programming to menu planning. EJOR 57, 339–347 (1992)CrossRefzbMATHGoogle Scholar
  2. 2.
    Darmon, N., Ferguson, E., Briend, A.: Linear and nonlinear programming to optimize the nutrient density of a population’s diet: an example based on diets of preschool children in rural Malawi. Am. J. Clin. Nutr. 75(2), 245–253 (2002)Google Scholar
  3. 3.
    Salookolayi, D.D., Yansari, A.T., Nasseri, S.H.: Application of fuzzy optimization in diet formulation. J. Math. Comput. Sci. 2(3), 459–468 (2011)Google Scholar
  4. 4.
    Cadenas, J.M., Pelta, D.A., Pelta, H.R., Verdegay, J.L.: Application of fuzzy optimization to diet problems in Argentinean farms. EJOR 158, 218–228 (2004)CrossRefzbMATHGoogle Scholar
  5. 5.
    Kashima, T., Matsumoto, S., Ishii, H.: Evaluation of menu planning capability based on multi-dimensional 0/1 knapsack problem of nutritional management system. IAENG IJAM 39(3), IJAM_39_3_04 (2009)Google Scholar
  6. 6.
    Davis, L.: Applying adaptive algorithms to epistatic domains. In: 9th International Joint Conference on Artificial Intelligence, pp. 162–164. Morgan Kaufmann Publishers Inc., San Francisco (1985)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Kindai UniversityHigashi-HiroshimaJapan
  2. 2.Hiroshima UniversityHigashi-HiroshimaJapan
  3. 3.Tokai UniversityHiratsukaJapan

Personalised recommendations