Advanced Computational Intelligence Paradigms in by Hiroyuki Yoshida, Ashlesha Jain, Ajita Ichalkaranje, Nikhil

By Hiroyuki Yoshida, Ashlesha Jain, Ajita Ichalkaranje, Nikhil Ichalkaranje

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Statistical analysis of the distribution of the potential alleles (drinks) in the best solutions (lunches) and the mean occurrence of the alleles (A,B) on which the rules were imposed 44 B. Ga´ al et al. 3 Application of Artificial Intelligence for Weekly Dietary Menu Planning 45 120,00% occurrence of A 100,00% 80,00% 60,00% 40,00% 20,00% 0,00% 100 50, 00% 75, B 100 ,00 % 00% 75, % ,00 0% 0 50, 00% 25, 00% 25, 00% A Fig. 6. The relative occurrence of a particular solution (A) in function of the strictness of two rules (penalizing solution A and B) 500 450 occurrence count 400 350 300 100% 250 75% 200 50% 150 25% 100 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 allele Fig.

Secondly, the harmony of the meal’s components should be considered. Plans satisfying nutritional constraints should also be appetizing. The dishes of a meal should go together. By common sense some dishes or nutrients do not appeal in the way others do. This common sense of taste and cuisine should be incorporated in any nutritional counselor designed for practical use. There could also be conflicting numerical constraints or harmony rules. A study found that even menus made by professionals may fail to satisfy all of the nutrient constraints [3].

Com 28 B. Ga´ al et al. kind of problems. Human professionals possibly surpass computer algorithms in quality, although research comparing performance has been ongoing since the 1960’s. The core idea of our algorithm is the hierarchical organization and parallel solution of the problem. Through the decomposition of the weekly menu planning problem, nutrient constraints can be satisfied on the level of meals, daily plans and weekly plans simultaneously. This feature, which is a novelty, makes the implementation of our method instantly applicable in practice.

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