Variação Genética e ganho esperado na seleção de progênies de Pinus caribaea var. caribaea em Selvíria, MS

The present research has proposed to estimate the genetic variation of growth traits and to estimate the expected gain by multi-effect index (MEI), in order to transform a Pinus caribaea var. caribaea progeny trial into a seedling seed orchard. The progeny trial was set up in 1989, in Selviria, MS, Brazil, using a 10 x 10 triple lattice design, with 99 progenies and a commercial control, with linear plots of ten plants, by the 3 x 3 m spacing between plants and rows. Total plant height, diameter at breast height (Dbh), wood volume, stem form, wood density at breast height, and survival were the evaluated quantitative traits. The trial was measured through 14, 15, and 16 years old. The 50% intensity of thinning at 14.3 years old was done. No significant was the genetic variation of different traits. Heritability estimates have presented low magnitude with low variation by the different ages. The application of MEI to DBH, at two years after thinning, resulted in higher gains than the selection of within and among progenies. The best selection strategy to obtain higher gains and to keep genetic diversity is to select until five plants per progenies.

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

MoSAC: um módulo para seleção de configurações arquiteturais baseado em critérios de QoS no contexto de sistema distribuídos autoadaptativos

The process for choosing the best components to build systems has become increasingly complex. It becomes more critical if it was need to consider many combinations of components in the context of an architectural configuration. These circumstances occur, mainly, when we have to deal with systems involving critical requirements, such as the timing constraints in distributed multimedia systems, the network bandwidth in mobile applications or even the reliability in real-time systems. This work proposes a process of dynamic selection of architectural configurations based on non-functional requirements criteria of the system, which can be used during a dynamic adaptation. This proposal uses the MAUT theory (Multi-Attribute Utility Theory) for decision making from a finite set of possibilities, which involve multiple criteria to be analyzed. Additionally, it was proposed a metamodel which can be used to describe the application s requirements in terms of the non-functional requirements criteria and their expected values, to express them in order to make the selection of the desired configuration. As a proof of concept, it was implemented a module that performs the dynamic choice of configurations, the MoSAC. This module was implemented using a component-based development approach (CBD), performing a selection of architectural configurations based on the proposed selection process involving multiple criteria. This work also presents a case study where an application was developed in the context of Digital TV to evaluate the time spent on the module to return a valid configuration to be used in a middleware with autoadaptative features, the middleware AdaptTV

Seleção de atributos em comitês de classificadores utilizando algoritmos genéticos

Classifier ensembles are systems composed of a set of individual classifiers and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account since there is no gain in combining identical classification methods. The ideal situation is a set of individual classifiers with uncorrelated errors. In other words, the individual classifiers should be diverse among themselves. One way of increasing diversity is to provide different datasets (patterns and/or attributes) for the individual classifiers. The diversity is increased because the individual classifiers will perform the same task (classification of the same input patterns) but they will be built using different subsets of patterns and/or attributes. The majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this investigation, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of homogenous and heterogeneous ensembles. The experiments will be divided into two phases that use a filter approach of feature selection guided by genetic algorithm