Neste trabalho foi realizada uma proposta de utilização de Sistemas de Inferência Nebulosos para controlar, em tempo de execução, parâmetros de Algoritmos Genéticos. Esta utilização busca melhorar o desempenho de Algoritmos Genéticos diminuindo, ao mesmo tempo: a média de iterações necessárias para que um Algoritmo Genético encontre o valor ótimo global procurado; bem como diminuindo o número de execuções do mesmo que não são capazes de encontrar o valor ótimo global procurado, nem mesmo para quantidades elevadas de iterações. Para isso, foram analisados os resultados de diversos experimentos com Algoritmos Genéticos, resolvendo instâncias dos problemas de Minimização de Funções e do Caixeiro Viajante, sob diferentes configurações de parâmetros. Com base nos resultados obtidos a partir destes experimentos, foi proposto um modelo com a troca de valores de parâmetros de Algoritmos Genéticos, em tempo de execução, pela utilização de Sistemas de Inferência Nebulosos, de forma a melhorar o desempenho do sistema, minimizando ambas as medidas citadas anteriormente. / This work addressed a proposal of the application of Fuzzy Systems to adjust parameters of Genetic Algorithms, during execution time. This application attempts to improve the performance of Genetic Algorithms by diminishing, at the same time: the average number of necessary generations for a Genetic Algorithm to find the desired global optimum value, as well as diminishing the number of executions of a Genetic Algorithm that are not capable of finding the desired global optimum value even for high numbers of generations. For that purpose, the results of many experiments with Genetic Algorithms were analyzed; addressing instances of the Function Minimization and the Travelling Salesman problems, under different parameter configurations. With the results obtained from these experiments, a model was proposed, for the exchange of parameter values of Genetic Algorithms, in execution time, by using Fuzzy Systems, in order to improve the performance of the system, minimizing both of the measures previously cited.

Neste trabalho foi realizada uma busca por relações, regras e transformações entre duas metodologias constituintes da Inteligência Computacional - a Computação Nebulosa e a Computação Evolutiva. Com a organização e sistematização da existência de tais transformações, obtém-se uma mudança na modelagem de soluções que as utilizam de forma conjunta, possibilitando que teorias e modelos bem estabelecidos em uma das metodologias possam ser aproveitados pela outra de uma forma mais robusta, correta por construção, intrínseca e transparente. Um modelo foi proposto para direcionar o trabalho de pesquisa. Através da análise desse modelo e de uma revisão bibliográfica realizada, transformações pontuais entre as metodologias foram elencadas, e posteriormente consolidadas por meio de experimentos práticos: uma Base de Conhecimento (BC) de um Controlador Lógico Nebuloso foi criada e modificada, conforme a necessidade, através de um Algoritmo Genético (AG). Com a abordagem desenvolvida, além da criação de BCs a partir de pouquíssimo conhecimento sobre o domínio do problema, tornou-se possível a inserção de novos \"comportamentos desejados\" em BCs já existentes, automaticamente, através de AGs. Os resultados desses experimentos, realizados sobre uma plataforma computacional especificada e implementada para este fim, foram apresentados e analisados. / This work addressed a search of relations, rules and transformations between two Computational Intelligence constituent methodologies - Fuzzy Computing and Evolutionary Computing. The existence of these relations changes the actual way of solutions modeling that uses these methodologies, allowing the utilization of well established theories and models of one technique by the other in a more robust, intrinsic and transparent way. Besides the research and systematization of points that indicate the existence of relations between the two methodologies, a model to guide these exploration was proposed. By this model analysis and by the bibliographic revision made, punctual transformations were pointed out, and further consolidated through practical experiments: a Knowledge Base (KB) of a Fuzzy Logic Controller was created and modified automatically by a Genetic Algorithm. With the developed approach, besides the creation of KBs, it became possible to automatically insert new \"desired behaviors\" to existent KBs. The results of such experiments, realized through a computational platform specified and implemented to this task, were presented and analyzed.

Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Therefore, effective prediction of future electricity consumption cannot be underestimated. Notably, repeated imbalance is noticed between the demand and supply of electricity, and this is affected by different weather profiles such as temperature, wind speed, dew point, humidity and pressure of the electricity consumption locations. Effective planning is therefore needed to aid electricity distribution among consumers. Such effective planning is activated by the need to predict future electricity consumption within a short period and the effect of weather variables on the predictions. Although state-of-the-art techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops and deploys a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT) model to address the lacuna of significant predictive error faced by the state-of-the-art models and to analyse the effect of each weather profile on the cooperative model. The PSA-DT is a machine learning model based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model with weather profiles outperforms the state-of-the-art models in terms of accuracy to a minimal error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes. / School of Computing / M. Sc. (Computer Science)

[pt] Este trabalho investiga métodos de detecção de falhas baseados em sistemas imunológicos artificiais, especificamente aqueles baseados no algoritmo de seleção negativa (NSA) e em outras técnicas de reconhecimento próprio/nãopróprio. Inicialmente, foi proposto um esquema de representação baseado em hiperesferas com centros e raios variáveis e três modelos capazes de gerar detectores, com esta representação, de forma eficiente. O primeiro modelo utiliza algoritmos genéticos onde cada gene do cromossomo contém um índice para um ponto de uma distribuição quasi-aleatória que servirá como centro do detector e uma função decodificadora responsável por determinar os raios apropriados. A aptidão do cromossomo é dada por uma estimativa do volume coberto através uma integral de Monte Carlo. O segundo modelo utiliza o particionamento Quadtree para gerar o posicionamento dos detectores e o valor dos raios. Este modelo pode realizar o particionamento a partir de uma função de detecção ou através de divisões recursivas de um detector inicial que ocupa todo o espaço. O terceiro modelo é inspirado nas redes imunológicas. Neste modelo, as células B representam os detectores e a rede formada por eles dá a posição e o raio de cada detector. Experimentos com dados sintéticos e reais demonstram a capacidade dos algoritmos propostos e que eles apresentam melhorias nos aspectos de escalabilidade e desempenho na detecção de falhas. / [en] This work investigates fault detection methods based on Artificial Immune Systems, specifically the negative selection algorithm (NSA) and other self/nonself recognition techniques. First, there was proposed a representation scheme based on hyperspheres with variable center and radius, and three models, which are very capable to generate detectors, based on that representation scheme, in an effective way. The first model employs Genetic Algorithms where each chromosome gene represents an index to a point in a quasi- random distribution, that will serve as a detector center, a decoder function will be responsible to determine the appropriate radius. The chromosome fitness is given by a valuation of the covered volume, which is calculated through a Monte Carlo integral. The second model uses the Quadtree space partition technique to generate the detectors positions and their radius. The space partition could be done by using a detection function or by recursive divisions of an initial detector that occupies the whole space. In third model, inspired on immune networks, the B cells represent the detectors and the network that is established by them gives the location and radius of each detector. Experiments with syntetic and real data show that the proposed algorithms improve scalability and perform better in fault detection.

The thesis presents research on multiobjective optimization based on memetic computing and its applications in engineering. We have introduced a framework for adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the selection, clustering, and local refinements. A robust stopping criterion for AMMOA has also been introduced to solve non-linear and large-scale optimization problems. The framework has been implemented for different benchmark test problems with remarkable results. This thesis also presents two applications of these algorithms. First, an optimal image data hiding technique has been formulated as a multiobjective optimization problem with conflicting objectives. In particular, trade-off factors in designing an optimal image data hiding are investigated to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNN) and the adaptive multiobjective memetic optimization algorithm (AMMOA) to solve this challenging problem. This novel image data hiding approach has been implemented for many different test natural images with remarkable robustness and transparency of the embedded logo watermark. We also introduce a perceptual measure based on the relative Rényi information spectrum to evaluate the quality of watermarked images. The second application is the problem of joint spectrum sensing and power control optimization for a multichannel, multiple-user cognitive radio network. We investigated trade-off factors in designing efficient spectrum sensing techniques to maximize the throughput and minimize the interference. To maximize the throughput of secondary users and minimize the interference to primary users, we propose a joint determination of the sensing and transmission parameters of the secondary users, such as sensing times, decision threshold vectors, and power allocation vectors. There is a conflict between these two objectives, thus a multiobjective optimization problem is used again in the form of AMMOA. This algorithm learns to find optimal spectrum sensing times, decision threshold vectors, and power allocation vectors to maximize the averaged opportunistic throughput and minimize the averaged interference to the cognitive radio network. / February 2016

Σκοπός της διατριβής είναι η ανάλυση, η μελέτη και η μοντελοποίηση της συμπεριφοράς τόσο των ευφυών πρακτόρων όσο και των χρηστών σε εικονικά περιβάλλοντα μάθησης, με τη χρήση τεχνικών υπολογιστικής νοημοσύνης. Το θεματικό αντικείμενο της διδακτορικής διατριβής αποτελεί ένα σύγχρονο αντικείμενο βασικής έρευνας με μεγάλο εύρος πρακτικών εφαρμογών. Η βάση της ερευνητικής δραστηριότητας εστιάζεται σε δύο βασικούς τομείς: 1. Προσαρμόσιμη μοντελοποίηση συμπεριφορών ευφυών πρακτόρων σε εικονικά περιβάλλοντα μάθησης, σύμφωνα με κανόνες βελτιστοποίησης της μαθησιακής επίδρασης στο χρήστη μέσα στο εικονικό περιβάλλον μάθησης. 2. Μοντελοποίηση χρηστών εικονικών περιβαλλόντων μάθησης, με στόχο τη βελτιστοποίηση της μαθησιακής επίδρασης στο χρήστη. Για τη μοντελοποίηση, τόσο της συμπεριφοράς των ευφυών πρακτόρων, όσο και των χρηστών, χρησιμοποιήθηκαν προηγμένες τεχνικές υπολογιστικής νοημοσύνης (Bayesian Δίκτυα, Γενετικοί και Εξελικτικοί Αλγόριθμοι). Αυτές οι τεχνικές, εκτός από την ευφυΐα, ενσωματώνουν και το επιθυμητό χαρακτηριστικό της προσαρμοσιμότητας, με την έννοια ότι μπορούν να προσαρμόζονται στις αλλαγές του περιβάλλοντος. Τα παραπάνω αποτελέσματα αξιολογήθηκαν στη χρήση τους σε Ευφυή Εικονικά Συστήματα Μάθησης βασισμένα στο Web (Intelligent Virtual Learning Systems – IVLS), τα οποία αποτελούν ουσιαστικά το μέσον εξαγωγής συμπερασμάτων και υποστηρικτικού υλικού για τη μετρήσιμη συμπεριφορά τόσο των ευφυών πρακτόρων όσο και των χρηστών, μέσα σε τέτοια περιβάλλοντα. / The main objectives of the thesis are the analysis, study and the provision of a behavior modeling procedure of the intelligent agents and the students in virtual e-learning systems using computational intelligence techniques. The domain of the thesis is a topic of basic research with a large scale of applied results. The basis of the research is focused in two main sectors: 1. Adaptive behavior modeling of intelligent agents in virtual learning systems, according to specific optimization rules of the learning process during the interaction of the user/student with the e-learning environment. 2. User modeling of the users of virtual learning environments towards the optimization of the learning process. For the modeling procedure of the behavior of intelligent agents and of the users specific computational intelligence techniques have been applied (Bayesian Networks, Genetic και Evolutionary Algorithms). The specific techniques provide intelligence to the system and the most important the feature of adaptability. The aforementioned results have been evaluated on Intelligent Virtual Learning Systems, which constitute the medium for the inference of the results and the mean for supportive material for the measurable behavior of the intelligent agents and of the users in Intelligent Virtual Learning Systems.

Αυτή η Διδακτορική Διατριβή πραγματεύεται το θέμα της ομαδοποίησης δεδομένων (clustering), καθώς και εφαρμογές των τεχνικών αυτών σε πραγματικά προβλήματα. Η παρουσίαση των επιμέρους θεμάτων και αποτελεσμάτων της διατριβής αυτής οργανώνεται ως εξής: Στο Κεφάλαιο 1 παρέχουμε τον ορισμό της Υπολογιστικής Νοημοσύνης σαν τομέας ερευνάς, και αναλύουμε τα ξεχωριστά τμήματα που τον αποτελούν. Για κάθε ένα από αυτά παρουσιάζεται μια σύντομη περιγραφή. Το Κεφάλαιο 2, ασχολείται με την ανάλυση του ερευνητικού πεδίου της ομαδοποίησης. Κάθε ένα από τα χαρακτηριστικά της αναλύεται ξεχωριστά και γίνεται μια επισκόπηση των σημαντικότερων αλγόριθμων ομαδοποίησης. Το Κεφάλαιο 3, αφιερώνεται στη παρουσίαση του αλγορίθμου UKW, που κατά την εκτέλεση του έχει την ικανότητα να προσεγγίζει το πλήθος των ομάδων σε ένα σύνολο δεδομένων. Επίσης παρουσιάζονται πειραματικά αποτελέσματα με σκοπό τη μελέτη της απόδοσης του αλγορίθμου. Στο Κεφάλαιο 4, προτείνεται μια επέκταση του αλγορίθμου UKW, σε μετρικούς χώρους. Η προτεινόμενη επέκταση διατηρεί όλα τα πλεονεκτήματα του αλγορίθμου UKW. Τα πειραματικά αποτελέσματα που παρουσιάζονται επίσης σε αυτό το κεφάλαιο, συγκρίνουν την προτεινόμενη επέκταση με άλλους αλγορίθμους. Στο επόμενο κεφάλαιο παρουσιάζουμε τροποποιήσεις του αλγορίθμου με στόχο την βελτίωση των αποτελεσμάτων του. Οι προτεινόμενες τροποποιήσεις αξιοποιούν πληροφορία από τα τοπικά χαρακτηριστικά των δεδομένων, ώστε να κατευθύνουν όσο το δυνατόν καλύτερα την αλγοριθμική διαδικασία. Το Κεφάλαιο 6, πραγματεύεται επεκτάσεις του αλγορίθμου σε κατανεμημένες Βάσεις δεδομένων. Για τις διάφορες υποθέσεις που μπορούν να γίνουν όσον αφορά τη φύση του περιβάλλοντος επικοινωνίας, παρουσιάζονται κατάλληλοι αλγόριθμοι. Στο Κεφάλαιο 7, εξετάζουμε την περίπτωση δυναμικών βάσεων δεδομένων. Σε ένα τέτοιο μη στατικό περιβάλλον αναπτύσσεται μια επέκταση του αλγορίθμου UKW, που ενσωματώνει τη δυναμική δομή δεικτοδότησης Bkd-tree. Επιπλέον παρουσιάζονται θεωρητικά αποτελέσματα για την πολυπλοκότητα χειρότερης περίπτωσης του αλγορίθμου. Το Κεφάλαιο 8, μελετά την εφαρμογή αλγορίθμων ομαδοποίησης σε δεδομένα γονιδιακών εκφράσεων. Επίσης προτείνεται και αξιολογείται ένα υβριδικό σχήμα που καταφέρνει να αυτοματοποιήσει την όλη διαδικασία επιλογής γονιδίων και ομαδοποίησης. Τέλος, η παρουσίαση του ερευνητικού έργου αυτής της διατριβής ολοκληρώνεται στο Κεφάλαιο 9 που ασχολείται με την ανάπτυξη υβριδικών τεχνικών που συνδυάζουν την ομαδοποίηση και τα Τεχνητά Νευρωνικά Δίκτυα, και αναδεικνύει τις δυνατότητες τους σε δύο πραγματικά προβλήματα. / This Doctoral Dissertation appoints the issue of data Clustering, as well as the applications of these kind of methods in real world problems. The presentation of the individual results of this dissertation is organised as follows: In Chapter 1, the definition of Computational Intelligence is provided as a research area. For each distinct part of this area a short description is supplied. Chapter 2, deals with the analysis of the research area of Clustering per se, and its characteristics are analysed separably. Moreover, we provide a review of the most representative clustering algorithms. Chapter 3, is devoted to the presentation of the UKW algorithm, that is able to endogenously provide approximations for the number of clusters in a dataset, during its execution. Furthermore, the included experimental results demonstrate the algorithm's efficiency. In Chapter 4, an extension of the UKW algorithm to metric spaces is proposed. This extension preserves all the advantages of the original algorithm. The included experimental results compare the proposed extension to other approaches. In the next chapter we present modifications of the UKW algorithm that scope to improve its efficiency. This is performed through the utilisation of information from the local characteristics of the data, so as to direct more efficiently the whole clustering procedure. Chapter 6, deals with extensions of the algorithm in distributed data bases. For the various assumptions that can be postulated for the nature of the communication environment different algorithms are proposed. In Chapter 7, we consider the case of dynamic databases. In such a non-static environment, an algorithm is developed that draws form the principles of the UKW algorithm, and embodies the dynamic indexing Bkd-tree data structure. Moreover, theoretical results are presented regarding the worst case complexity of the algorithm. Chapter 8, studies the application of clustering algorithms in gene expression data. Besides, it is proposed and evaluated, a hybrid algorithmic scheme that manages to automate the whole procedure of gene selection and clustering. Finally, the presentation of the research work of this dissertation is fulfilled in Chapter 9. This Chapter is devoted to the development of hybrid techniques that combine clustering methods and Artificial Neural Networks, and demonstrate their abilities in two real world problems.

Οι τεχνικές Υπολογιστικής Νοημοσύνης βρίσκουν σε μεγάλο βαθμό εφαρμογή σε Ηλεκτρονικά Συστήματα Μάθησης. Στην εργασία αυτή υιοθετείται η τεχνική των Bayesian δικτύων. Αναλυτικότερα υλοποιείται ένα έξυπνο σύστημα το οποίο αναλαμβάνει τη διαχείριση των ερωτηματολογίων ενός Ηλεκτρονικού Συστήματος Μάθησης. Σκοπός της των Bayesian δικτύων είναι η «έξυπνη» διαχείριση των ερωτηματολογίων. Πιο συγκεκριμένα, πραγματοποιείται γραφική απεικόνιση των ερωτηματολογίων σε Bayesian γράφημα όπου κάθε ερώτηση αντιστοιχεί σε ένα κόμβο του γραφήματος. Στο γράφημα αυτό εφαρμόζονται οι εξισώσεις του Bayes σε κάθε κόμβο του γραφήματος ώστε να υπολογιστούν οι πιθανότητες επιτυχούς απάντησης μιας ερώτησης. Στη συνέχεια οι πιθανότητες συγκρίνονται με κατώφλια τα οποία ορίζει ο διαχειριστής του συστήματος ώστε να αποφευχθούν ερωτήσεις στις οποίες ο χρήστης έχει μεγάλη πιθανότητα να απαντήσει επιτυχώς. Επίτευγμα αυτής της υλοποίησης είναι η εξοικονόμηση ερωτήσεων και χρόνου εκ μέρους του χρήστη. Το δεύτερο μέρος της εργασίας αφορά στην επέκταση του παραπάνω συστήματος χρησιμοποιώντας την αρχιτεκτονική ευφυών πρακτόρων. Βασικός σκοπός της επέκτασης αυτής είναι η δυνατότητα διαχείρισης ενός μεγάλου αριθμού χρηστών και ερωτηματολογίων από απομακρυσμένα συστήματα. / In this contribution an innovative platform is being presented that integrates intelligent agents in legacy e-learning environments. It introduces the design and development of a scalable and interoperable integration platform supporting various assessment agents for e-learning environments. The agents are implemented in order to provide intelligent assessment services to computational intelligent techniques such as Bayesian Networks and Genetic Algorithms. The utilization of new and emerging technologies like web services allows integrating the provided services to any web based legacy e-learning environment.

Thesis (Ph.D.) -- Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

Made available in DSpace on 2016-08-31T13:33:39Z (GMT). No. of bitstreams: 1 EmmanuelRMD_DISSERT.pdf: 3141928 bytes, checksum: 608c0b50b8f95185573646571b1dde6f (MD5) Previous issue date: 2013-12-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A new tool based on the use of computational intelligence applied in control systems is presented. This type of application has attracted the interest of researchers due the advantages in relation to others methods for controlling settings, especially when the system has a complex dynamics to be adjusted by traditional methods. The proposed computational tool GACT (Genetic Algorithm Control Tool) was developed to work as a Toolbox of MATLAB® software for use in control systems and is based on the joint application of Genetic Algorithms (GA) and the classical control theory. The GACT based on the advancement of the operating systems that are no longer fully handled by command lines, and now have an interactive graphical user interface. That is, the referred Toolbox allows the implementation of an intelligent control system in a way more simplified and interactive. The graphical user interface (GUI) was designed using the software GUIDE, integrated with MATLAB® in order to connect with the source code and block diagrams of the system to be controlled at the SIMULINK® / Uma nova ferramenta baseada no uso da inteligência computacional aplicada em sistemas de controle é apresentada. Esse tipo de aplicação tem despertado o interesse de pesquisadores por apresentar vantagens em relação aos outros métodos de ajustes para controladores, principalmente quando o sistema apresenta uma dinâmica complexa de ser ajustada pelos métodos tradicionais. A ferramenta computacional proposta GACT (Genetic Algorithm Control Tool) foi desenvolvida para funcionar como uma Toolbox do software MATLAB® para aplicação em sistemas de controle e fundamenta-se na aplicação conjunta de Algoritmos Genéticos (GA do inglês Genetic Algorithm) com a teoria de controle clássico. O GACT baseia-se no avanço dos sistemas operacionais que deixaram de ser totalmente manuseados por linhas de comando e passaram a ter uma interface gráfica interativa. Ou seja, a referida Toolbox possibilita a implementação de um sistema de controle inteligente de maneira mais simplificada e interativa. A interface gráfica foi concebida através do software GUIDE, integrado ao MATLAB® de maneira a relacionar-se com os códigos fontes e os diagramas de blocos do sistema a ser controlado no SIMULINK®