ECOLOGICAL MODELING OF TALITROIDES TOPITOTUM (CRUSTACEA: AMPHIPODA)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Automatic visual dictionary generation through optimum-path forest clusteringautomatic visual dictionary generation through optimum-path forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation.

Uma proposta de resolução para problemas de roteirização de veículos baseada no algoritmo genético

The vehicle routing problem is to nd a better route to meet a set of customers who are geographically dispersed using vehicles that are a central repository to which they return after serving customers. These customers have a demand that must be met. Such problems have a wide practical application among them we can mention: school transport, distribution of newspapers, garbage collection, among others. Because it is a classic problem as NP-hard, these problems have aroused interest in the search for viable methods of resolution. In this paper we use the Genetic Algorithm as a resolution

Modelos matemáticos e computacionais em dengue

This work was developed starting the study of traditionals mathematical models that describe the epidemiology of infectious díseases by direct or indirect transmission. We did the classical approach of equilibrium solutions search, its analysis of stability analytically and by numerical solutions. After, we applied these techniques in a compartimental model of Dengue transmission that consider the mosquito population (susceptible vector Vs and 'infected vector VI), human population (suseeptíble humans S, infected humans I and recovered humans R) and just one sorotype floating in this population. We found the equilibrium solutions and from their analises, it was possible find the reprodution rate of dísease and which define if the disease will be endemic or not in the population.- ext, we used the method described a..~, [1] to study the infíuence of seasonalíty at vírus transmission, when it just acts on one of rates related with the vector. Lastly, we made de modeling considering the periodicity of alI rates, thereby building, a modeI with temporal dependence that permits to study periodicity of transmission through of the approach of parametrical ressonance and genetic algorithm

Novo algoritimo genético para problemas de cobertura de conjuntos

The Set Covering Problem (SCP) plays an important role in Operational Research since it can be found as part of several real-world problems. In this work we report the use of a genetic algorithm to solve SCP. The algorithm starts with a population chosen by a randomized greedy algorithm. A new crossover operator and a new adaptive mutation operator were incorporated into the algorithm to intensify the search. Our algorithm was tested for a class of non-unicost SCP obtained from OR-Library without applying reduction techniques. The algorithms found good solutions in terms of quality and computational time. The results reveal that the proposed algorithm is able to find a high quality solution and is faster than recently published approaches algorithm is able to find a high quality solution and is faster than recently published approaches using the OR-Library.

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Um problema de corte de estoque multiobjetivo

In this paper we deal with the one-dimensional integer cutting stock problem, which consists of cutting a set of available objects in stock in order to produce ordered smaller items in such a way as to optimize a given objective function, which in this paper is composed of three different objectives: minimization of the number of objects to be cut (raw material), minimization of the number of different cutting patterns (setup time), minimization of the number of saw cycles (optimization of the saw productivity). For solving this complex problem we adopt a multiobjective approach in which we adapt, for the problem studied, a symbiotic genetic algorithm proposed in the literature. Some theoretical and computational results are presented.

Computational speed-up with a single qudit

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

Diferentes métodos de aglutinação para melhoria de processos com múltiplas respostas

Pós-graduação em Engenharia Mecânica - FEG

Estudo da dinâmica de populações utilizando a técnica de polinômios de colocação

Pós-graduação em Engenharia Elétrica - FEB

A matheuristic for the selection of beam directions and dose distribution in radiotherapy planning

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Metaheuristics for a crop rotation problem

This paper presents a mathematical model adapted from literature for the crop rotation problem with demand constraints (CRP-D). The main aim of the present work is to study metaheuristics and their performance in a real context. The proposed algorithms for solution of the CRP-D are a genetic algorithm, a simulated annealing and hybrid approaches: a genetic algorithm with simulated annealing and a genetic algorithm with local search algorithm. A new constructive heuristic was also developed to provide initial solutions for the metaheuristics. Computational experiments were performed using a real planting area and semi-randomly generated instances created by varying the number, positions and dimensions of the lots. The computational results showed that these algorithms determined good feasible solutions in a short computing time as compared with the time spent to get optimal solutions, thus proving their efficacy for dealing with this practical application of the CRP-D.