Portifolio selection with random transaction costs

Transaction costs have a random component in the bid-ask spread. Facing a high bid-ask spread, the consumer has the option to wait for better terms oI' trade, but only by carrying an undesirable portfolio balance. We present the best policy in this case. We pose the control problem and show that the value function is the uni que viscosity solution of the relevant variational inequality. Next, a numerical procedure for the problem is presented.

The open capacitated arc routing problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

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

Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.

Fish ladders select fish traits on migration - still a growing problem for natural fish populations

We investigated the potential selective effect of fish ladders on physiological and morphological profiles of the curimbata, Prochilodus lineatus, during reproductive migration in Brazil. We registered sex, body weight and length, plasma glucose, hepatosomatic and gonadosomatic indices (HSI and GSI, respectively), hematocrit, leucocrit, blood cell and nucleus areas, and the diameter of white and red muscle fibers in fish sampled at the bottom (downstream) and at the top (upstream) of a fish ladder at a hydroelectric dam. Males and females at the top of the ladder showed higher size (weight and length), white muscle fiber diameters, plasma glucose levels and lower hematocrit when compared with those at the bottom. These size and muscle traits assist fish to overcome the ladder barrier and bypass the dam, an effort that might be reflected in the glucose levels. Females also showed higher GSI at the top of the fish ladder, a trait possibly facilitating their reproduction upstream. These results indicate that a dam system favors fish with specific morphological-physiological profile. This may have a strong influence upon upstream fish populations over generations and implies the presence of artificial selective pressure.

Multimedia selection: Allowing educators to act critically and independently

In Brazil, important portals like the Portal do Professor, or Teacher's Portal, from the Ministry of Education, offer multimedia products like audios, videos, games, animations, simulations and others with an accompanying teacher's guide. These guides in general suggest ways to prepare the students to use the products while offering indications on how to practice that knowledge after using the products in the classrooom. Despite this, portals with huge repositories that receive new products every week don't present to teachers a solution for a problem: How to select the appropriate products to use in the classroom and how to assess their use after teaching in order to check if the learning was meaningful? In this way, this paper discusses multimedia selection for meaningful learning while considering concept mapping and abstraction classification. The development of multimedia repositories has created both opportunities for easy access to digital content and areas of serious concerns since the misuse of products by teachers may lead to different problems.

Improving Parkinson's disease identification through evolutionary-based feature selection

Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. © 2011 IEEE.

Identification and feature selection of non-technical losses for industrial consumers using the software WEKA

This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. © 2012 IEEE.

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.

Selection of desulfurizing agents and optimization of operational variables in hot metal desulfurization

The growing demand for steels with tighter compositional specifications led the Companhia Siderúrgica Nacional (CSN) to develop more efficient processes. To solve this problem this paper aims to identify the operational variables more impacting in the desulfurization process, specifically in torpedo car, as well as its causes and solutions. Then select and test, with laboratorial and industrial tests, desulfurizing agents based of CaC 2, CaO, CaCO3, and Mg to assess the cost per quantity of product desulfurized. The mixture with best results was not that one with highest content of CaC2. It is believed that this mixture showed better efficiency because of the increased agitation of the bath, produced by the releasing of gas from compound CaCO3 present in this mixture. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimal conductor size selection and reconductoring in radial distribution systems using a mixed-integer LP approach

This paper presents a mixed-integer linear programming model to solve the conductor size selection and reconductoring problem in radial distribution systems. In the proposed model, the steady-state operation of the radial distribution system is modeled through linear expressions. The use of a mixed-integer linear model guarantees convergence to optimality using existing optimization software. The proposed model and a heuristic are used to obtain the Pareto front of the conductor size selection and reconductoring problem considering two different objective functions. The results of one test system and two real distribution systems are presented in order to show the accuracy as well as the efficiency of the proposed solution technique. © 1969-2012 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.

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.