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Minimum and maximum ranges for nutrient intakes

Minimum and maximum ranges for nutrient intakes

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Encouraging healthy and balanced diet plans is one of the most important action points for governments around the world. Generating healthy, balanced and inexpensive menu plans that fulfil all the recommendations given by nutritionists is a complex and time-consuming task; because of this, computer science has an important role in this area. This p...

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... on the recommended amount, a range of acceptable intake is generated for each nutrient. Table 2 defines a set R of pairs (r min , r max ) with the minimum and maximum amount allowed for each nutrient h, respectively (The set of micro-nutrients is as follows: Folic acid, Phosphorus, Magnesium, Selenium, Sodium, Vitamins A, B1, B2, B6, B12, C, D, E, Iodine, Zinc). Formally, an individual S would be considered feasible if and only if it satisfies the following set of global constraints: ...

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... In the work of (Syahputra et al. 2017) a Genetic Algorithm is used to schedule diets for diabetic patients. (Marrero et al. 2020) compared the use of a Memetic Algorithm (MA) and an Iterated Local Search combined with a Multi-Objective Evolutionary Algorithm based on Decomposition (ILS-MOEA/D) to generate healthy, balanced and inexpensive lunch menu plans for a school cafeteria, while (Hernandez-Ocana et al. 2018) used a Two Swim Modified Bacterial Foraging Optimization Algorithm TSM-BFOA 'to minimize the difference between the number of calories required by an individual and the number of calories provided by the healthy menu found applying TS-MBFOA'. The use of the MPP as the basis for the development of food systems in health care institutions, prisons, schools or the hospitality industry has also been quite common (Lancaster 1992;Sufahani and Ismail 2014;Moreira et al. 2018;Aggarwal et al. 2020). ...
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Promoting healthy lifestyles is nowadays a public priority among most public entities. The ability to design an array of nutritious and appealing diets is very valuable. Menu Planning still presents a challenge which complexity derives from the problems’ many dimensions and the idiosyncrasies of human behavior towards eating. Among the difficulties encountered by researchers when facing the Menu Planning Problem, being able of finding a rich feasible region stands out. We consider it as a system of inequalities to which we try to find solutions. We have developed and implemented a two-phase algorithm -that mainly stems from the Randomized Search and the Genetic- that is capable of rapidly finding an pool of solutions to the system with the aim of properly identifying the feasible region of the underlying problem and proceed to its densification. It consists of a hybrid algorithm inspired on a GRASP metaheuristic and a later recombination. First, it generates initial seeds, identifying best candidates and guiding the search to create solutions to the system, thus attempting to verify every inequality. Afterwards, the recombination of different promising candidates helps in the densification of the feasible region with new solutions. This methodology is an adaptation of other previously used in literature, and that we apply to the MPP. For this, we generated a database of a 227 recipes and 272 ingredients. Applying this methodology to the database, we are able to obtain a pool of feasible (healthy and nutritious) complete menus for a given D number of days.
... HernÃAE'à †â€™Ãƒâ€šÃ‚¡ndez et al. [28] employed goal programming to generate low-cost healthy diets following Mediterranean standards. Besides, Marrero et al. [38] and Ramos-Perez et al. [47] used multi-objective evolutionary algorithms to formulate diets healthily over a period that minimizes the diet's cost and the level of repetition of food groups. Moreover, [53,54] and [55] focused on computing optimized healthily calorie-restricted diets through evolutionary algorithms. ...
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The most popular and successful way to maintain a healthy body is to have a rich and balanced diet combined with physical exercise. Since the diet dilemma was proposed, several works in the literature suggested calculating a diet that respects the nutritional needs of each person. In the Caloric-Restricted Diet Problem (CRDP), the goal is to find a reduced-calorie diet that meets these nutritional needs, enabling weight loss. This paper proposes an Island-Based Hybrid Evolutionary Algorithm (IBHEA) that uses a Genetic Algorithm (GA) and a Differential Evolution (DE) Algorithm with different parameters settings in different islands communicating through several migration policies to solve the CRDP. Computational experiments showed that IBHEA outperformed more than 5% compared with non-distributed and non-hybrid implementations, generating a greater variety of diets with a small calorie count.
... The memetic algorithm was used to minimize the cost of the meal plans. The same set of global and daily constraints were also considered in a multiobjective formulation of the MPP recently proposed [41], where the single-objective memetic algorithm introduced in Reference [40] was compared, by performing a wide experimental assessment, to a novel multi-objective memetic approach based on the well-known Multiobjective Evolutionary Algorithm by Decomposition (MOEA/D) [42]. With respect to the objective functions optimized, the cost and the variety of specific courses and food groups that the meal plans consist of were considered. ...
... At this point, we should note that, in past research, the diversity of courses and food groups has been considered as constraints [12,31]. Considering it as an objective function is a novel approach addressed by the authors [41]. ...
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A multi-objective formulation of the Menu Planning Problem, which is termed the Multi-objective Menu Planning Problem, is presented herein. Menu planning is of great interest in the health field due to the importance of proper nutrition in today’s society, and particularly, in school canteens. In addition to considering the cost of the meal plan as the classic objective to be minimized, we also introduce a second objective aimed at minimizing the degree of repetition of courses and food groups that a particular meal plan consists of. The motivation behind this particular multi-objective formulation is to offer a meal plan that is not only affordable but also varied and balanced from a nutritional standpoint. The plan is designed for a given number of days and ensures that the specific nutritional requirements of school-age children are satisfied. The main goal of the current work is to demonstrate the multi-objective nature of the said formulation, through a comprehensive experimental assessment carried out over a set of multi-objective evolutionary algorithms applied to different instances. At the same time, we are also interested in validating the multi-objective formulation by performing quantitative and qualitative analyses of the solutions attained when solving it. Computational results show the multi-objective nature of the said formulation, as well as that it allows suitable meal plans to be obtained.