Dynamic Preference-Based Diet Optimization

• Since the classic diet planning problem, several mathematical models and programming techniques have been shown to generate menus following several constraints.
• However, an approach focused on nutritional counseling proves to be more effective than common restrictive diets, by adapting to the habits and preferences of users.
• In this project we propose a new dynamic optimization optimization_model considering preferences predictions.

Work in Progress...

• Unit tests for nutrition database - ON GOING
• User preferences class and unit tests
• User preference database and unit tests
• User targets and unit tests
• User diet and unit tests
• User EA and unit tests
• Benchmarks

• Change the enum Model (.../test/benchmark/main.cpp) replacing by one of current available.

• nutritional facts based on TACO (http://www.nepa.unicamp.br/taco/contar/taco_4_edicao_ampliada_e_revisada.pdf?arquivo=taco_4_versao_ampliada_e_revisada.pdf)
• objective function: minimize caloric difference against target:

• nutritional deviation values:

• nutrition facts separated by categories with different portion size:

• penalties applied according to nutritional deviations intensity:

• result: a list of foods to be eat freely during the day.

• separate dayle recommendation menu into 6 different mealtimes:

• penalties formulation applied according to nutritional deviations proportionally to mealtime (the penalties are applied according to mealstime caloric target percentage of total):

• result: a list of food considering mealtimes proportional (caloric and nutrients).

• user interactions after each mealtime inputting.caloric deviation consume difference of user consume input and mealtime menu recommended calculation:

• compensatory system to equilibrate/recalculate further mealtimes considering current user consume:

• result: dynamic menu that changes/recalculate according to user input on each mealtime

• prediction instance (consume history database) based on mealtime category preferences instance generation rule:

PS: The category preference was used due a limitation provided due no real consume database availability actually. It is necessary to create a real food consume per mealtime database in ten future...

• item-item score matrix created using collaborative filtering

  magnitude calculation:

  

  item importance:

  

  cosine similarity calculation:

  

  final vector score of items in a specific mealtimes for an user:

  

• first-fit algorithm to include high scored items limited on 50% of mealtimes caloric target.

• populate TACO DB with prices based on ENAPE.
• (on going) change the objective function to maximizing and evaluation function to cost minimization + nutritional deviation:

• caloric value as a nutrient range like others using 1600-2400 for women and 2000-3000 for men.

• initial random population
• pattern select scale/strategy: windows/tournament
• child select scale/strategy: windows/tournament
• mutation type: flip resetting
• mutation_strength: 0.4
• crossover_probability: 0.6

• expand the capacity of the caloric compensation mechanism by adding physical exercises for cases in which there is an excess caloric intake where it is not possible to compensate.
• easy database change using cereal lib for txt/csv nutrition facts serialize or sql DB connection for MYSQL relational db query
• dynamic minimization cost considering
• different solvers

• Dissertation available on http://