https://repositorio.ufu.br/handle/123456789/17903 2026-09-17T17:48:52Z https://repositorio.ufu.br/handle/123456789/50302 Title: Desenvolvimento de plataforma para auxiliar nas aulas de anatomia da madeira 2026-05-07T00:00:00Z https://repositorio.ufu.br/handle/123456789/50297 Title: Investigação de diferentes abordagens do Algoritmo Evolutivo aplicado ao problema de predição de proteínas no modelo HP-3D Abstract: Evolutionary Algorithms (EAs) are metaheuristics inspired by biological evolution, widely used to solve complex optimization problems, especially those involving multiple conflicting objectives. This work aims to investigate and compare the performance of different EA variants, considering both single-objective and multiobjective approaches, applied to the Protein Structure Prediction (PSP) problem using the three-dimensional hydrophobic-polar (HP-3D) model. To this end, different approaches were computationally implemented and evaluated, highlighting multiobjective variants based on multiple tables, such as the AEMMT. The algorithms were evaluated using metrics such as solution fitness, number of evaluations, and success rate. The results show that multiobjective approaches outperform single-objective ones, especially in more complex instances. As a main contribution, this study provides a systematic comparative analysis that identifies the most robust and efficient algorithmic configurations for the problem in the HP-3D model, corroborating the literature and reinforcing the potential of multiobjective EAs for this application. 2026-03-19T00:00:00Z https://repositorio.ufu.br/handle/123456789/50296 Title: Otimização de agentes inteligentes no jogo Ludo por meio de um Algoritmo Genético Abstract: Genetic Algorithms (GAs) are optimization techniques inspired by the principles of natural evolution, widely used in solving complex problems characterized by large search spaces and high variability. In the context of board games, especially those involving randomness and interaction between multiple players, the manual definition of effective strategies becomes a significant challenge. This work investigates the application of Genetic Algorithms in optimizing the decision parameters of an intelligent agent in the game Ludo, a stochastic environment in which probabilistic factors, such as dice rolls, directly influence the course of the games. To this end, a logic simulation environment and an agent based on weighted heuristics were implemented, whose strategy is encoded by a chromosome composed of five real genes. These genes represent weights associated with different strategic aspects of the game, such as progress, attack, safety, risk, and completion. The Genetic Algorithm is used to evolve these weights, and systematic experimental analyses are performed on different evolutionary configurations, including population size, number of generations, selection methods, elitism, crossover, and mutation. The results obtained demonstrated that the Genetic Algorithm is capable of evolving agents with consistent performance superior to that of random agents, exhibiting stable convergence and low variability between independent runs. The analysis of the winning DNAs reveals recurring strategic patterns, indicating the emergence of coherent and interpretable behaviors. Thus, the study highlighted the potential of Genetic Algorithms as an effective approach for optimizing intelligent agents in stochastic games, in addition to contributing to the practical understanding of the impact of genetic operators in this type of problem. 2026-03-14T00:00:00Z https://repositorio.ufu.br/handle/123456789/50120 Title: Analise visual dos gastos públicos municipais: estudo aplicado a cidade de Uberlândia 2026-07-31T00:00:00Z