Algoritmo Q-learning como estratégia de exploração e/ou explotação para metaheurísticas GRASP e algoritmo genético

Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of xploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique - Q-learning Algorithm - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) and Genetic Algorithm. The GRASP metaheuristic uses Q-learning instead of the traditional greedy-random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. In the Genetic Algorithm, the Q-learning algorithm was used to generate an initial population of high fitness, and after a determined number of generations, where the rate of diversity of the population is less than a certain limit L, it also was applied to supply one of the parents to be used in the genetic crossover operator. Another significant change in the hybrid genetic algorithm is the proposal of a mutually interactive cooperation process between the genetic operators and the Q-learning algorithm. In this interactive/cooperative process, the Q-learning algorithm receives an additional update in the matrix of Q-values based on the current best solution of the Genetic Algorithm. The computational experiments presented in this thesis compares the results obtained with the implementation of traditional versions of GRASP metaheuristic and Genetic Algorithm, with those obtained using the proposed hybrid methods. Both algorithms had been applied successfully to the symmetrical Traveling Salesman Problem, which was modeled as a Markov decision process

Avaliação clínica e comparativa da oclusão balanceada bilateral e desoclusão com guia canino sobre a função mastigatória de usuários de prótese total removível

The maintenance of masticatory function is especially important for patients who wear complete dentures due to the limitations of this type of prosthesis. Thus, the bilateral balanced occlusion (BBO) is used to achieve, besides other advantages, greater masticatory efficiency. However, analyzing critically the literature, it is observed that there is not enough scientific evidence that support the BBO as the most appropriate occlusal concept in complete dentures. This way, the purpose of the present study was to verify if complete dentures wearers with BBO present better masticatory efficiency and capacity than those with canine guidance (CG). A double-blind controlled crossover clinical trial was conducted. The sample was made of 24 completely edentulous patients. The subjects wore sets of complete dentures with both occlusal concepts for equal periods of 3 months. Objective data were collected through the masticatory efficiency test, performed by the colorimetric method, in which capsules of a synthetic material enclosing fuchsine- containing granules were used. Subjective data were recorded by patient´s ratings of their chewing function, which is the masticatory ability. No significant statistical difference was found for masticatory efficiency (p=0,0952) and masticatory ability (x2=0,5711/ p=0,4498) between the two occlusal concepts studied, as well as there was no correlation between these two variables (p=0,2985). Based on these results, it seems reasonable to use CG for the setup of complete dentures, since it is an easier and quicker technical procedure, until that future researches can come to complement this question