Permutation-based Recombination Operator to Node-depth Encoding

AbstractThe node-depth encoding is a representation for evolutionary algorithms applied to tree problems. Its represents trees by storing the nodes and their depth in a proper ordered list. The original formulation of the node-depth encoding has only mutation operators as the search mechanism. Although the representation has this restriction, it has obtained good results with low convergence. Then, this work proposes a specific recombination operator to improve the convergence of the node-depth encoding representation. These operators are based on recombination for permutation representations. An investigation into the bias and heritability of the proposed recombination operator shows that it has a bias towards stars and low heritability. The performance of node-depth encoding with the proposed operator is investigated for the optimal communication spanning tree problem. The results are presented for benchmark instances in the literature. The use of the recombination operator results in a faster convergence than with only mutation operators

Genetic Algorithm for Variable and Samples Selection in Multivariate Calibration Problems

One of the main problems of quantitative analytical chemistry is to estimate the concentration of one or more species from the values of certain physicochemical properties of the system of interest. For this it is necessary to construct a calibration model, i.e., to determine the relationship between measured properties and concentrations. The multivariate calibration is one of the most successful combinations of statistical methods to chemical data, both in analytical chemistry and in theoretical chemistry. Among used methods can cite Artificial Neural Networks (ANN), the Nonlinear Partial Least Squares (N-PLS), Principal Components Regression (PCR) and Multiple Linear Regression (MLR). In addition of multivariate calibration methods algorithms of samples selection are used. These algorithms choose a subset of samples to be used in training set covering adequately the space of the samples. In other hand, a large spectrum of a sample is typically measured by modern scanning instruments generating hundreds of variables. Search algorithms have been used to identify variables which contribute useful information about the dependent variable in the model. This paper proposes a Genetic Algorithm based on Double Chromosome (GADC) to do these tasks simultaneously, the sample and variable selection. The obtained results were compared with the well-known algorithms for samples and variable selection Kennard-Stone, Partial Least Square and Successive Projection Algorithm. We showed that the proposed algorithm can obtain better calibrations models in a case study involving the determination of content protein in wheat samples

Microscopic Image Segmentation to Quantification of Leishmania Infection in Macrophages

The determination of infection rate parameter from in vitro macrophages infected by Leishmania amastigotes is fundamental in the study of vaccine candidates and new drugs for the treatment of leishmaniasis. The conventional method that consists in the amastigotes count inside macrophages, normally is done by a trained microscope technician, which is liable to misinterpretation and sampling. The objective of this work is to develop a method for the segmentation of images to enable the automatic calculation of the infection rate by amastigotes. Segmentation is based on mathematical morphology in the context of a computer vision system. The results obtained by computer vision system presents a 95% accuracy in comparison to the conventional method. Therefore, the proposed method can contribute to the speed and accuracy of analysis of infection rate, minimizing errors from the traditional methods, especially in situations where exhaustive repetitions of the procedure are required from the technician.A determinação de parâmetros como taxa de infecção em monocultura de macrófagos cultivados in vitro com Leishmania é fundamental no estudo de candidatos vacinais e novos fármacos para o tratamento de leishmanioses. O método convencional que consiste na contagem de amastigotas no interior de macrófagos, normalmente é realizada por um especialista treinado em microscopia óptica, o que está sujeito a erros de interpretação e amostragem. O objetivo do trabalho é desenvolver um método para a segmentação de imagens como etapa preliminar para o cálculo automático da taxa de infecção por amastigotas. A segmentação é baseada em morfologia matemática no contexto de um sistema de visão computacional. Os resultados obtidos pelo método computacional demonstraram acerto de 95% quando comparados ao método convencional. Conclui-se que a metodologia computacional baseada na segmentação de imagem como pré-requisito para o cálculo de taxa de infecção, pode contribuir para a rapidez e a precisão na obtenção dos resultados e na minimização de erros cometidos no método tradicional, especialmente em situações em que exaustivas repetições do procedimento são exigidas ao observador

Evolutionary algorithms, to proteins structures prediction

A Determinação da Estrutura tridimensional de Proteínas (DEP) a partir da sua seqüência de aminoácidos é importante para a engenharia de proteínas e o desenvolvimento de novos fármacos. Uma alternativa para este problema tem sido a aplicação de técnicas de computação evolutiva. As abordagens utilizando Algoritmos Evolutivos (AEs) tem obtido resultados relevantes, porém estão restritas a pequenas proteínas, com dezenas de aminoácidos e a algumas classes de proteínas. Este trabalho propõe a investigação de uma abordagem utilizando AEs para a predição da estrutura terciária de proteínas independentemente do seu tamanho e classe. Os resultados obtidos demonstram que apesar das dificuldades encontradas a abordagem investigada constitue-se em uma alternativa em relação aos métodos clássicos de determinação da estrutura terciária das proteínas.Protein structure determination (DEP) from aminoacid sequences is very importante to protein engineering and development of new drugs. Evolutionary computation has been aplied to this problem with relevant results. Nevertheless, Evolutionary Algorithms (EAs) can work with only proteins with few aminoacids and some protein classes. This work proposes an approach using AEs to predict protein tertiary structure independly from their size and class. The obtained results show that, despite of the difficulties that have been found, the investigate approach is a relevant alternative to classical methods to protein structure determination

Efficient Data Structures to Evolutionary Algorithms Applied to Network Design Problems.

Problemas de projeto de redes (PPRs) são muito importantes uma vez que envolvem uma série de aplicações em áreas da engenharia e ciências. Para solucionar as limitações de algoritmos convencionais para PPRs que envolvem redes complexas do mundo real (em geral modeladas por grafos completos ou mesmo esparsos de larga-escala), heurísticas, como os algoritmos evolutivos (EAs), têm sido investigadas. Trabalhos recentes têm mostrado que estruturas de dados adequadas podem melhorar significativamente o desempenho de EAs para PPRs. Uma dessas estruturas de dados é a representação nó-profundidade (NDE, do inglês Node-depth Encoding). Em geral, a aplicação de EAs com a NDE tem apresentado resultados relevantes para PPRs de larga-escala. Este trabalho investiga o desenvolvimento de uma nova representação, baseada na NDE, chamada representação nó-profundidade-grau (NDDE, do inglês Node-depth-degree Encoding). A NDDE é composta por melhorias nos operadores existentes da NDE e pelo desenvolvimento de novos operadores de reprodução possibilitando a recombinação de soluções. Nesse sentido, desenvolveu-se um operador de recombinação capaz de lidar com grafos não-completos e completos, chamado EHR (do inglês, Evolutionary History Recombination Operator). Foram também desenvolvidos operadores de recombinação que lidam somente com grafos completos, chamados de NOX e NPBX. Tais melhorias tem como objetivo manter relativamente baixa a complexidade computacional dos operadores para aumentar o desempenho de EAs para PPRs de larga-escala. A análise de propriedades de representações mostrou que a NDDE possui redundância, assim, foram propostos mecanismos para evitá-la. Essa análise mostrou também que o EHR possui baixa complexidade de tempo e não possui tendência, além de revelar que o NOX e o NPBX possuem uma tendência para árvores com topologia de estrela. A aplicação de EAs usando a NDDE para PPRs clássicos envolvendo grafos completos, tais como árvore geradora de comunicação ótima, árvore geradora mínima com restrição de grau e uma árvore máxima, mostrou que, quanto maior o tamanho das instâncias do PPR, melhor é o desempenho relativo da técnica em comparação com os resultados obtidos com outros EAs para PPRs da literatura. Além desses problemas, um EA utilizando a NDE com o operador EHR foi aplicado ao PPR do mundo real de reconfiguração de sistemas de distribuição de energia elétrica (envolvendo grafos esparsos). Os resultados mostram que o EHR possibilita reduzir significativamente o tempo de convergência do EANetwork design problems (NDPs) are very important since they involve several applications from areas of Engineering and Sciences. In order to solve the limitations of traditional algorithms for NDPs that involve real world complex networks (in general, modeled by large-scale complete or sparse graphs), heuristics, such as evolutionary algorithms (EAs), have been investigated. Recent researches have shown that appropriate data structures can improve EA performance when applied to NDPs. One of these data structures is the Node-depth Encoding (NDE). In general, the performance of EAs with NDE has presented relevant results for large-scale NDPs. This thesis investigates the development of a new representation, based on NDE, called Node-depth-degree Encoding (NDDE). The NDDE is composed for improvements of the NDE operators and the development of new reproduction operators that enable the recombination of solutions. In this way, we developed a recombination operator to work with both non-complete and complete graphs, called EHR (Evolutionary History Recombination Operator). We also developed two other operators to work only with complete graphs, named NOX and NPBX. These improvements have the advantage of retaining the computational complexity of the operators relatively low in order to improve the EA performance. The analysis of representation properties have shown that NDDE is a redundant representation and, for this reason, we proposed some strategies to avoid it. This analysis also showed that EHR has low running time and it does not have bias, moreover, it revealed that NOX and NPBX have bias to trees like stars. The application of an EA using the NDDE to classic NDPs, such as, optimal communication spanning tree, degree-constraint minimum spanning tree and one-max tree, showed that the larger the instance is, the better the performance will be in comparison whit other EAs applied to NDPs in the literatura. An EA using the NDE with EHR was applied to a real-world NDP of reconfiguration of energy distribution systems. The results showed that EHR significantly decrease the convergence time of the E

Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.FAPESPBrazilian Research Foundation of the State of Sao PauloBrazilian Agency CNP