## 1 Introduction  
A person is considered overweight when her/his Body Mass Index (BMI) is higher than 25 and obese if it is over 30. BMI is computed by dividing the weight of the person (in kilograms) by the square of her/his height (in meters) [1]. According to the Spanish National Institute of Statistics [2], the rate of individuals with obesity has increased from 7.4% in 1987 to 17.4% in 2017. In just 30 years, it has multiplied by 2.4 times, taking the data for Spain as a reference. This health problem affects all sectors of the population, although it is not equally distributed. In particular, men are more prone to develop overweight/obesity than women. The number of cases of childhood obesity has also increased, reaching 10.3% of children between 2 and 17 years old. The World Health Organization [3] also shows that overweight people are more prone to developing cerebrovascular and respiratory problems, gallbladder disease and may increase the risk of different types of cancer. Hence, it is necessary to prevent future cases of overweight or obesity. Some people may have a certain predisposition to suffer from overweight/obesity due to genetics. Overweight and obesity are usually a consequence of social behaviors, such as high-fat meals and sedentary lifestyles. Prevention should begin at early ages to consolidate healthy lifestyle habits, and those who suffer from any of these disorders should seek the help and advice of medical staff. Regarding prevention, it would be essential to know the relationships among lifestyle indicators, overweight, and obesity. In this context, a system predicting the risk of developing overweight and obesity could be very useful. In this paper, we show that it is possible to analyze the different factors and habits of a person and obtain the relationship among them by machine-learning techniques. In particular, we investigate the performance of a set of machine-learning classification algorithms when classifying people as overweight/obese versus non-overweight/non-obese using information about lifestyle habits. This information was collected by a pool of 14 different institutions participating in the consortium of the GenObIA project (work financed by the Community of Madrid and the European Social Fund through the GENOBIA-CM project with reference S2017/BMD-3773). In machine learning (ML), classification is related to the assignment of class labels to data in the problem domain. A critical element in this type of problem is the selection of the variables used as predictors (feature selection). Using all variables is not always the best option; sometimes it is necessary to discard part of them in order to avoid noise in the data and also to reduce computational load. Feature-selection (FS) methods aim to reduce dimensionality, reduce execution times, and improve model results. In this work, four different approaches for FS have been tested, firstly, without applying FS, secondly by classical FS, and finally two FS methods using genetic algorithms (GAs). In particular, we investigate FS by a GA, using two selection operators: a classical tournament selection, and _Stud_ selection, a new method proposed in this paper. Experimental results show that machine-learning algorithms are good classifiers when combined with evolutionary feature-selection methods for this particular problem. The main contributions of this work are:  
* We tried different configurations for a set of ML classifiers for people being overweight or obese based on information concerning lifestyle and dietary habits. * We use a dataset with data of more than 1170 people, which is the largest study in Spain to the best of our knowledge. * We explore four different feature-selection methods. * We propose the _Stud_ selection operator, a variant of the _stud GA_ algorithm presented in [4], that could be adapted to another evolutionary algorithm. The rest of the paper is organized as follows. Section 2 provides a brief review of related work.