Bacharelado em Sistemas de Informação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/12
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APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso
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Item Alocação otimizada de horários acadêmicos com disponibilidade restrita de professores usando algoritmos genéticos(2022-06-01) Demiro, Matheus Paulo dos Santos; Garrozi, Cícero; http://lattes.cnpq.br/0488054917286587; http://lattes.cnpq.br/8926398361586659The generation of academic timetables is one of the most complex and arduous activities faced by educational institutions at the beginning of each academic period. In most cases, the solution found for this problem, commonly called “timetabling” in the literature, is performed manually, which makes the process very tiring and time-consuming for institutions. This problem is considered a great challenge in combinatorial optimization, due to the wide set of variables and constraints involved, being considered an NP-Complete problem, where there is no possibility of solution through conventional programming methods. This article deals with the use of genetic algorithm techniques to find an optimal solution to the problem of scheduling academic schedules that takes into account the restrictions of the student and the faculty, in order to favor the academic performance of students and adapt to availability from the students . teachers. For this work, it is expected to develop a genetic algorithm that is able to obtain valid results that meet the constraints of the problem in a reasonably considerable time. Technically, it is expected that the algorithm, from a set of input data, processes and returns a solution that has the highest fitness value - the lowest number of infractions committed - between generations of individuals (solutions). This article uses data from the Information Systems course grid at the Federal University of Rio Grande Norte as a base. After modifications in the base and the experiments were carried out, the genetic algorithm proved to be efficient and managed to achieve the objectives, generating adequate academic schedules and compatible with the established restrictions.Item Otimização de equipes em League of Legends utilizando algoritmos genéticos multiobjetivo(2022-06-03) Vieira, Lucas Marsol; Garrozi, Cícero; http://lattes.cnpq.br/0488054917286587League of Legends, a game of the Multiplayer Online Battle Arena or MOBA category (as it is popularly known), continues to be one of the highest paying electronic games in the world. This category is based on two teams that face each other on a symmetrical map with the objective of destroying the opposing base. One of the main points in this style of play and more specifically in League of Legends is the character selection stage (also known as champions), as it will guide the strategy of each team. In this step, players select which characters they will use within the game, where each character has characteristics and abilities that are different from the others. As it involves several factors in the selection process, it is considered a complex problem that can be solved with search techniques and artificial intelligence to find the best solutions. In this project, a new approach through Multi-Objective Evolutionary Algorithms (MOEA) is presented to generate teams in the game. In order to estimate the quality of the generated teams, a survey was conducted with a group of players. Significant results were achieved with this approach, obtaining an average rating of 4.5 for a total of 5 points.