A comparison of statistical methods for genomic selection in a mice population

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Post-selection and return of foraged material by Acromyrmex subterraneus brunneus (Hymenoptera: Formicidae)

Two colonies of Acromyrmex subterraneus brunneus Forel (Hymenoptera: Formicidae) were studied regarding their behavior during cultivation of the fungus garden to determine a) the existence of post-selection of foraged material by the workers, and b) if present, the mechanism of this discrimination and how this material is returned. Many studies on plant processing by leaf-cutting ants have been carried out, but none of them has investigated the decision-making process of workers in the case of erroneous food selection. For this purpose, material with different degrees of moisture and hardness (floral sponge, polystyrene, plastic and clay) were individually offered to the colonies and the tasks performed by the different size categories were carefully recorded. Three tasks, i.e., foraging, cultivation of the fungus garden and return of the foraged material, were studied and subdivided into 14 subtasks. Analysis of all inert materials as a whole showed the presence of post-selection of foraged material through the return of material inadequate for the workers and the fungus. Discrimination of the inert material was observed at the time of shredding, probably based on parameters such as physical resistance to cutting and moisture content. A. s. brunneus workers showed flexibility in their activities during substrate processing. The observed post-selection of foraged material provides strong evidence for the cognitive abilities of worker ants and of the colony as a whole. Polymorphism and a complex society represent vital characteristics for the ecological success of this species.

BBA: A binary bat algorithm for feature selection

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper approach combines the power of exploration of the bats together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in five public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based techniques. © 2012 IEEE.

Binary Bat Algorithm for Feature Selection

Feature selection aims to find the most important information to save computational efforts and data storage. We formulated this task as a combinatorial optimization problem since the exponential growth of possible solutions makes an exhaustive search infeasible. In this work, we propose a new nature-inspired feature selection technique based on bats behavior, namely, binary bat algorithm The wrapper approach combines the power of exploration of the bats together with the speed of the optimum-path forest classifier to find a better data representation. Experiments in public datasets have shown that the proposed technique can indeed improve the effectiveness of the optimum-path forest and outperform some well-known swarm-based techniques. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.

Optimizing feature selection through binary charged system search

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be inviable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the Charged System Search (CSS), which has never been applied to this context so far. The wrapper approach combines the power of exploration of CSS together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in four public datasets have demonstrated the validity of the proposed approach can outperform some well-known swarm-based techniques. © 2013 Springer-Verlag.

Site Selection for Restoration Planning: A Protocol With Landscape and Legislation Based Alternatives

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

On the optical flow model selection through metaheuristics

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)