Abstract: Constraint Satisfaction Problems allow one to expressively model problems. On the other hand, propositional satisfiability problem (SAT) solvers can handle huge SAT instances. We thus present a technique to expressively model set constraint problems and to encode them automatically into SAT instances. Our technique is expressive and less error-prone. We apply it to the Social Golfer Problem and to symmetry breaking of the problem.

Abstract: This paper proposes multi-objective genetic algorithm for the problem of variable selection in multivariate calibration. We consider the problem related to the classification of biodiesel samples to detect adulteration, Linear Discriminant Analysis classifier. The goal of the multi-objective algorithm is to reduce the dimensionality of the original set of variables; thus, the classification model can be less sensitive, providing a better generalization capacity. In particular, in this paper we adopted a version of the Non-dominated Sorting Genetic Algorithm (NSGA-II) and compare it to a mono-objective Genetic Algorithm (GA) in terms of sensitivity in the presence of noise. Results show that the mono-objective selects 20 variables on average and presents an error rate of 14%. One the other hand, the multi-objective selects 7 variables and has an error rate of 11%. Consequently, we show that the multi-objective formulation provides classification models with lower sensitivity to the instrumental noise when compared to the mono-objetive formulation.

Abstract: The selection of the lowest number of observers that ensures the maximum visual coverage over an area represented by a digital elevation model (DEM) is an important problem with great interest in many elds, e.g., telecommunications, environment planning, among others. However, this problem is complex and intractable when the number of points of the DEM is relatively high. This complexity is due to three issues: 1) the diculty in determining the visibility of the territory from a point, 2) the need to know the visibility at all points of the territory and 3) the combinatorial complexity of the selection of observers. The recent progress in total-viewshed maps computation not only provides an ecient solu-tion to the rst two problems, but also opens other ways to new solutions that were unthinkable previously. This paper presents a new type of cartography, called the masked total viewshed map, and based on this algorithm, optimal solutions for both sequential and simultaneous observers location are provided.

Abstract: Today, a lot of applications with incorporated Geo Positional Systems (GPS) deliver huge quantities of spatio-temporal data. Trajectories followed by moving objects can be generated from this data. However, these trajectories may have silent durations, i.e., time durations when no data are available for describing the route of a MO. As a result, the movement during silent durations must be described and the low sampling data trajectory need to be filled in using specialized techniques of data imputation to study and discover new knowledge based on movement. Our interest is to show opportunities of analytical tasks using a criteria based operator over reconstructed low-sampling trajectories. Also, a simple visual analysis of the reconstructed trajectories is done to offer a simple analytic perspective of the reconstruction and how the criterion of movement can change the analysis. To the best of our knowledge, this work is the first attempt to use the different reconstruction of trajectories criteria to identify the opportunities of analytical tasks over reconstructed low-sampling trajectories as a whole.

Abstract: Data Envelopment Analysis (DEA) is a non-parametric technique for estimating the technical efficiency of a set of Decision Making Units (DMUs) from a database consisting of inputs and outputs. This paper studies DEA models based on maximizing technical efficiency, which aim to determine the least distance from the evaluated DMU to the production frontier. Usually, these models have been solved through unsatisfactory methods used for combinatorial NP-hard problems. Here, the problem is approached by metaheuristic techniques and the solutions are compared with those of the methodology based on the determination of all the facets of the frontier in DEA. The use of metaheuristics provides solutions close to the optimum with low execution time.