This thesis discusses the development and performance of a novel evolutionary algorithm, the Targeted Estimation of Distribution Algorithm (TEDA). TEDA takes the concept of targeting, an idea that has previously been shown to be effective as part of a Genetic Algorithm (GA) called Fitness Directed Crossover (FDC), and introduces it into a novel hybrid algorithm that transitions from a GA to an Estimation of Distribution Algorithm (EDA). Targeting is a process for solving optimisation problems where there is a concept of control points, genes that can be said to be active, and where the total number of control points found within a solution is as important as where they are located. When generating a new solution an algorithm that uses targeting must first of all choose the number of control points to set in the new solution before choosing which to set. The hybrid approach is designed to take advantage of the ability of EDAs to exploit patterns within the population to effectively locate the global optimum while avoiding the tendency of EDAs to prematurely converge. This is achieved by initially using a GA to effectively explore the search space before transitioning into an EDA as the population converges on the region of the global optimum. As targeting places an extra restriction on the solutions produced by specifying their size, combining it with the hybrid approach allows TEDA to produce solutions that are of an optimal size and of a higher quality than would be found using a GA alone without risking a loss of diversity. TEDA is tested on three different problem domains. These are optimal control of cancer chemotherapy, network routing and Feature Subset Selection (FSS). Of these problems, TEDA showed consistent advantage over standard EAs in the routing problem and demonstrated that it is able to find good solutions faster than untargeted EAs and non evolutionary approaches at the FSS problem. It did not demonstrate any advantage over other approaches when applied to chemotherapy. The FSS domain demonstrated that in large and noisy problems TEDA’s targeting derived ability to reduce the size of the search space significantly increased the speed with which good solutions could be found. The routing domain demonstrated that, where the ideal number of control points is deceptive, both targeting and the exploitative capabilities of an EDA are needed, making TEDA a more effective approach than both untargeted approaches and FDC. Additionally, in none of the problems was TEDA seen to perform significantly worse than any alternative approaches.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2018-01-15T21:33:32Z No. of bitstreams: 1 AmandaLucenaGermano_DISSERT.pdf: 7217536 bytes, checksum: 25a20d10202bb0af3f3b8e89539d2fbb (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2018-01-17T12:52:40Z (GMT) No. of bitstreams: 1 AmandaLucenaGermano_DISSERT.pdf: 7217536 bytes, checksum: 25a20d10202bb0af3f3b8e89539d2fbb (MD5) / Made available in DSpace on 2018-01-17T12:52:40Z (GMT). No. of bitstreams: 1 AmandaLucenaGermano_DISSERT.pdf: 7217536 bytes, checksum: 25a20d10202bb0af3f3b8e89539d2fbb (MD5) Previous issue date: 2017-07-31 / Com a necessidade do aumento da qualidade dos produtos e do desempenho dos processos, o grau de automa??o cresceu bastante nas ind?strias. Com isso, os sistemas est?o cada vez mais complexos e v?m acompanhados por problemas dif?ceis de resolver devido ? alta dimensionalidade desses sistemas e do grande volume do fluxo de informa??es necess?rias, al?m da aleatoriedade de falhas e defeitos. Uma falha inesperada pode levar a riscos operacionais, por isso a import?ncia de detectar e localizar a falha, principalmente quando a planta industrial ainda est? operando em uma regi?o control?vel e ? poss?vel agir para trazer o processo de volta para o estado normal, seguro e operacional. Assim, ? desej?vel que o sistema de detec??o de falhas forne?a respostas r?pidas e confi?veis com um esfor?o computacional adequado para processamento em tempo real, mesmo necessitando tratar com grandes quantidades de dados. Para trabalhar com grandes quantidades de dados em tempo real, surgiu o modelo de fluxo de dados, que consiste de uma sequ?ncia ordenada de pontos que s? podem ser lidos apenas uma ou algumas poucas vezes. Essa ?rea cresceu bastante nos ?ltimos anos, principalmente devido a grande quantidade de sistemas que precisavam tratar com dados desse tipo, que incluem desde dados do mercado financeiro, registros telef?nicos, transa??es web a dados m?dicos, redes de sensores ou mesmo dados multim?dia. Diante da relev?ncia do tema de detec??o de falhas, nessa tese foram utilizados o TEDA (Typicality and Eccentricity Data Analytics), o RDE (Recursive Density Estimation) e o R-PCA (Recursive Principal Component Analysis) como ferramentas para detec??o de falhas em processos industriais. Para a an?lise do desempenho de cada uma dessas abordagens foi utilizado o cl?ssico benchmark Tennessee Eastman Process. / In order to increase product quality and process performance, the degree of automation has grown significantly in industries. As a result, systems are increasingly complex and are accompanied by problems that are difficult to solve due to the high dimensionality of these systems and the large amount of information flow, as well as the randomness of faults and defects. An unexpected failure can lead to operational risks, so the importance of detecting and locating the fault, especially when the industrial plant is still operating in a controllable region and it is possible to act to bring the process back to normal, safe and operational. Thus, it is desirable for the fault detection system to provide fast and reliable responses with a computational effort appropriate for real-time processing, even though it requires handling large amounts of data. In this context, data stream-oriented algorithms to outlier detection may be promising candidates for fault detection of industrial process, because they work with sequences of temporarily ordered samples. In addition, they handle well with large amount of data because they are recursive and online algorithms that do not need to store past samples. Thus, in this dissertation two algorithms of this class are analyzed, named TEDA (Typicality and Eccentricity Data Analytics) and RDE (Recursive Density Estimation), when applied to fault detection of industrial processes. Their performances are compared to R-PCA (Recursive Principal Component Analysis) algorithm. The classic Tennessee Eastman Process benchmark was used as case study to evaluate these algorithms.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-11-22T20:38:08Z No. of bitstreams: 1 ClauberGomesBezerra_TESE.pdf: 7864722 bytes, checksum: 17c21362443f4d25511a0a211d52b805 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-11-23T23:24:44Z (GMT) No. of bitstreams: 1 ClauberGomesBezerra_TESE.pdf: 7864722 bytes, checksum: 17c21362443f4d25511a0a211d52b805 (MD5) / Made available in DSpace on 2017-11-23T23:24:44Z (GMT). No. of bitstreams: 1 ClauberGomesBezerra_TESE.pdf: 7864722 bytes, checksum: 17c21362443f4d25511a0a211d52b805 (MD5) Previous issue date: 2017-05-24 / Nesta tese apresentamos uma nova abordagem para realizar o agrupamento e a classifica??o de um conjunto de dados de forma n?o supervisionada. A abordagem proposta utiliza os conceitos de tipicidade e excentricidade usados pelo algoritmo TEDA na detec??o de outliers. Para realizar o agrupamento e a classifica??o ? proposto um algoritmo estat?stico chamado Auto-Cloud. As amostras analisadas pelo Auto-Cloud s?o agrupadas em unidades chamadas de data clouds, que s?o estruturas que n?o possuem formato ou limites definidos. O Auto-Cloud permite que cada amostra analisada possa pertencer simultaneamente a v?rias data clouds. O Auto-Cloud ? um algoritmo aut?nomo e evolutivo, que n?o necessita de treinamento ou qualquer conhecimento pr?vios sobre o conjunto de dados analisado. Ele permite a cria??o e a fus?o das data clouds de forma aut?noma, ? medida que as amostras s?o lidas, sem qualquer interven??o humana. As caracter?sticas do algoritmo fazem com que ele seja indicado para o agrupamento e classifica??o de streams de dados e para aplica??es que requerem respostas em tempo-real. O Auto- Cloud tamb?m ? um algoritmo recursivo, o que o torna r?pido e exige pouca quantidade de mem?ria. J? no processo de classifica??o dos dados, o Auto-Cloud trabalha como um classificador fuzzy, calculando o grau de pertin?ncia entre a amostra analisada e cada data cloud criada no processo de agrupamento. A classe a que pertence cada amostra ? determinada pela data cloud com maior grau de pertin?ncia com rela??o a amostra. Para validar o m?todo proposto, aplicamos o mesmo em v?rios conjuntos de dados existentes na literatura sobre o assunto. Al?m disso, o m?todo tamb?m foi validado numa aplica??o de detec??o e classifica??o de falhas em processos industriais, onde foram utilizados dados reais, obtidos de uma planta industrial. / In this thesis we propose a new approach to unsupervised data clustering and classification. The proposed approach is based on typicality and eccentricity concepts. This concepts are used by recently introduced TEDA algorithm for outlier detection. To perform data clustering and classification, it is proposed a new statistical algorithm, called Auto-Cloud. The data samples analyzed by Auto-Cloud are grouped in the form of unities called data clouds, which are structures without pre-defined shape or boundaries. Auto-Cloud allows each data sample to belong to multiple data clouds simultaneously. Auto-Cloud is an autonomous and evolving algorithm, which does not requires previous training or any prior knowledge about the data set. Auto-Cloud is able to create and merge data clouds autonomously, as data samples are obtained, without any human interference. The algorithm is suitable for data clustering and classification of online data streams and application that require real-time response. Auto-Cloud is also recursive, which makes it fast and with little computational effort. The data classification process works like a fuzzy classifier using the degree of membership between the analyzed data sample to each data cloud created in clustering process. The class to which each data sample belongs is determined by the cloud with the highest activation with respect to that sample. To validate the proposed method, we apply it to several existing datasets for data clustering and classification. Moreover, the method was also used in a fault detection in industrial processes application. In this case, we use real data obtained from a real world industrial plant.

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