Este artigo investiga a aplicação de Algoritmos Genéticos na Otimização da Operação de Sistemas Hidrotérmicos de Potência, com objetivo de analisar a aplicabilidade dessa técnica em grandes sistemas. Este é um problema complexo, de grande porte, composto por várias usinas interligadas e pertencentes a diferentes cascatas. Os autores acreditam que, devido à sua simplicidade, paralelismo e generalidade, os Algoritmos Genéticos apresentam um grande potencial para a resolução do problema apontado. Os testes foram aplicados em dois conjuntos com usinas pertencentes ao Sistema Sudeste Brasileiro. O primeiro considera 19 usinas a reservatório, e o segundo é composto por 35 usinas, sendo 19 a reservatório e as restantes a fio d'água.

This paper investigates the application of Genetic Algorithms in the Optimization of the Operation Planning of Hydrothermal Power Systems. The aim of this work to analyze the application of this technique in a complex problem of high dimension, composed by several plants, belonging to different cascades. The authors believe that, due to their simplicity, parallelism and generality, the Genetic Algorithms present a great potential for the resolution of the pointed problem. The tests were applied in two groups with plants belonging to the Brazilian Southeast System. The first considers only reservoir hydroelectric, in the total of 19 plants, and the second uses reservoir hydroelectric the and run-of-river capacity hydroelectric plants, composed by 35 plants.

The Human Genome Project has generated a large amount of sequence data. A number of works are currently concerned with analyzing these data. One of the analyses carried out is the identification of genes' structures on the sequences obtained. As such, one can search for particular signals associated with gene expression. Splice junctions represent a type of signal present on eukaryote genes. Many studies have applied Machine Learning techniques in the recognition of such regions. However, most of the genetic databases are characterized by the presence of noisy data, which can affect the performance of the learning techniques. This paper evaluates the effectiveness of five data pre-processing algorithms in the elimination of noisy instances from two splice junction recognition datasets. After the pre-processing phase, two learning techniques, Decision Trees and Support Vector Machines, are employed in the recognition process.

The aim of data mining is to find useful knowledge inout of databases. In order to extract such knowledge, several methods can be used, among them machine learning (ML) algorithms. In this work we focus on ML algorithms that express the extracted knowledge in a symbolic form, such as rules. This representation may allow us to ''explain'' the data. Rule learning algorithms are mainly designed to induce classification rules that can predict new cases with high accuracy. However, these sorts of rules generally express common sense knowledge, resulting in many interesting and useful rules not being discovered. Furthermore, the domain independent biases, especially those related to the language used to express the induced knowledge, could induce rules that are difficult to understand. Exceptions might be used in order to overcome these drawbacks. Exceptions are defined as rules that contradict common believebeliefs. This kind of rules can play an important role in the process of understanding the underlying data as well as in making critical decisions. By contradicting the user's common beliefves, exceptions are bound to be interesting. This work proposes a method to find exceptions. In order to illustrate the potential of our approach, we apply the method in a real world data set to discover rules and exceptions in the HIV virus protein cleavage process. A good understanding of the process that generates this data plays an important role oin the research of cleavage inhibitors. We consider believe that the proposed approach may help the domain expert to further understand this process.