Abstract
Multi-objective optimization inspired on genetic algorithms are population based search methods. The population elements, chromosomes, evolve using inheritance, mutation, selection and crossover mechanisms. The aim of these algorithms is to obtain a representative non-dominated Pareto front from a given problem. Several approaches to study the convergence and performance of algorithm variants have been proposed, particularly by accessing the final population. In this work, a novel approach by analyzing multi-objective algorithm dynamics during the algorithm execution is considered. The results indicate that Shannon entropy can be used as an algorithm indicator of diversity and convergence.

Abstract
Multidimensional systems, or n-D systems, are systems having several independent variables. Several topics, in particular stability, of n-D systems (n > 1) have attracted the interest of many researchers. The main reason, is because the extension stability theory of 1-D systems to systems with higher dimensions is not straightforward. In this paper, two adopted meta-heuristics algorithms are used for complementing the study of systems stability based on their polynomial characteristics over the variables boundaries. The two meta-heuristics are genetic algorithm and particle swarm optimization due to its popularity. Practical results of both meta-heuristics are compared and the better algorithm highlighted. The results demonstrate that meta-heuristics can be applied in studding multidimensional system stability.

Abstract
Multi-objective optimization evolutionary techniques provide solutions for a specific problem using optimally concepts taking into consideration all the design criteria. In the last years, several multi-objective algorithms were proposed but usually the performance is measured at the end neglecting, therefore, the solution diversity along the interactions. In order to understand the evolution of the solutions this work studies the dynamic of the successive iterations. The analysis adopts the fractional entropy for measuring the statistical behavior of the population. The results show that the entropy is a good tool to monitor and capture phenomena such as the diversity and convergence during the algorithm execution. © Springer International Publishing AG 2017.

Abstract
Hill climbing and simulated annealing are two fundamental search techniques integrating most artificial intelligence and machine learning courses curricula. These techniques serve as introduction to stochastic and probabilistic based metaheuristics. Simulated annealing can be considered a hill-climbing variant with a probabilistic decision. While simulated annealing is conceptually a simple algorithm, in practice it can be difficult to parameterize. In order to promote a good simulated annealing algorithm perception by students, a simulation experiment is reported here. Key implementation issues are addressed, both for minimization and maximization problems. Simulation results are presented.

Abstract
This work presents a new approach to tune the parameters of the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Sequential Quadratic Programming (SQP), thus yielding a dual optimization scheme. Simulations and performance indexes for a non minimum linear model result in a better performance, improving robustness and tracking accuracy.

Abstract
This paper describes a proposal to develop a Tourism Recommendation System based in users enhanced profiles (composed by basic user information, relations between user and a set of stereotypes and user functionality levels). The main focus of this work is to evaluate if user's physical and psychological functionality levels considered in user's profiles creation, will produce significant changes in the recommendation results. This work aims also to contribute with a different way to classify points-of-interest (POI) considering their capacity to receive tourists with certain levels of physical and psychological issues that will be described in this paper sections. © 2016 ACM.