Evolvable Hardware (EHW) is a promising area in electronics today. Evolutionary Algorithms (EA), together with a circuit simulation tool or real hardware, automatically designs a circuit for a given problem. The circuits evolved may have unconventional designs and be less dependent on the personal knowledge of a designer. Nowadays, EA are represented by Genetic Algorithms (GA), Genetic Programming (GP) and Evolutionary Strategy (ES). While GA is definitely the most popular tool, GP has rapidly developed in recent years and is notable by its outstanding results. However, to date the use of ES for analogue circuit synthesis has been limited to a few applications. This work is devoted to exploring the potential of ES to create novel analogue designs. The narrative of the thesis starts with a framework of an ES-based system generating simple circuits, such as low pass filters. Then it continues with a step-by-step progression to increasingly sophisticated designs that require additional strength from the system. Finally, it describes the modernization of the system using novel techniques that enable the synthesis of complex multi-pin circuits that are newly evolved. It has been discovered that ES has strong power to synthesize analogue circuits. The circuits evolved in the first part of the thesis exceed similar results made previously using other techniques in a component economy, in the better functioning of the evolved circuits and in the computing power spent to reach the results. The target circuits for evolution in the second half are chosen by the author to challenge the capability of the developed system. By functioning, they do not belong to the conventional analogue domain but to applications that are usually adopted by digital circuits. To solve the design tasks, the system has been gradually developed to support the ability of evolving increasingly complex circuits. As a final result, a state-of-the-art ES-based system has been developed that possesses a novel mutation paradigm, with an ability to create, store and reuse substructures, to adapt the mutation, selection parameters and population size, utilize automatic incremental evolution and use the power of parallel computing. It has been discovered that with the ability to synthesis the most up-to-date multi-pin complex analogue circuits that have ever been automatically synthesized before, the system is capable of synthesizing circuits that are problematic for conventional design with application domains that lay beyond the conventional application domain for analogue circuits.

Vibrations of a wind turbine have a negative impact on its performance and therefore approaches to effectively control turbine vibrations are sought by wind industry. The body of previous research on wind turbine vibrations has focused on physics-based models. Such models come with limitations as some ideal assumptions do not reflect reality. In this Thesis a data-driven approach to analyze the wind turbine vibrations is introduced. Improvements in the data collection of information system allow collection of large volumes of industrial process data. Although the sufficient information is contained in collected data, they cannot be fully utilized to solve the challenging industrial modeling issues. Data-mining is a novel science offers platform to identify models or recognize patterns from large data set. Various successful applications of data mining proved its capability in extracting models accurately describing the processes of interest. The vibrations of a wind turbine originate at various sources. This Thesis focuses on mitigating vibrations with wind turbine control. Data mining algorithms are utilized to construct vibration models of a wind turbine that are represented by two parameters, drive train acceleration and tower acceleration. An evolutionary strategy algorithm is employed to optimize the wind turbine performance expressed with three objectives, power generation, vibration of wind turbine drive train, and vibration of wind turbine tower. The methodology presented in this Thesis is applicable to industrial processes other than wind industry.

Bright field cellular microscopy offers an image capturing method that is both non-invasive and simple to implement. However, the resulting micrographs pose challenges for image segmentation which are compounded when the subject cells are tightly clustered or overlapping. Filamentous cyanobacteria are a type of organism that grow as linearly arranged cells forming chain-like filaments. Existing methods for bright field cell segmentation perform poorly on micrographs of these bacteria, and are incapable of identifying the filaments. Existing filament tracking methods are rudimentary, and cannot reliably account for overlapping or parallel touching filaments. We propose a new approach for identifying filaments in bright field micrographs by combining information about both filaments and cells. This information is used by an evolutionary strategy to iteratively construct a continuous spline representation that tracks the medial line of the filaments. We demonstrate that overlapping and parallel touching filaments are handled appropriately in many difficult cases.

Die vorliegende Dissertation befasst sich mit der theoretischen Vorhersage neuer Materialien. Ein evolutionärer Algorithmus, der zur Lösung dieses globalen Optimierungsproblems Konzepte der natürlichen Evolution imitiert, wurde entwickelt und ist als Programmpaket EVO frei verfügbar. EVO findet zuverlässig sowohl bekannte als auch neuartige Kristallstrukturen. Beispielsweise wurden die Strukturen von Germaniumnitrofluorid, einer neue Borschicht und mit dem gekreuzten Graphen einer bisher unbekannte Kohlenstoffstruktur gefunden. Ferner wurde in der Arbeit gezeigt, dass das reine Auffinden solcher Strukturen der erste Teil einer erfolgreichen Vorhersage ist. Weitere aufwendige Berechnungen sind nötig, die Aufschluss über die Stabilität der hypothetischen Struktur geben und Aussagen über zu erwartende Materialeigenschaften liefern.

The work proposed in this dissertation is the field of combinatorial optimization, which aims to find a solution to these types of problems at a low computational time and effectively. The combinatorial optimization studies a set of discrete solutions, which have a finite number of elements, to find the best viable solution to the problems of this magnitude. One of the main approaches that area is the Traveling Salesman Problem (TSP), mainly due to the size of possible solutions to the problem, so that is intractable computation by exhaustive search methods. Given all these features, this work is to study and develop evolutionary strategies for the resolution of the Problem of Traveling Salesman with Heterogeneous Demands (TSPHD), a variation of the classic TSP. The evolutionary strategies belong to the class of evolutionary computation, and methods of search based on the theory of the evolution of species, where the best individuals compete for survival. The evolutionary strategies differ from other optimization techniques, as the search is conducted in a population of solutions, not a single point. To solve the problem are proposed four evolutionary algorithms, using heuristics techniques and metaheurísticas for its implementation. The results were obtained from tests using instances of low density (low connection), and compared with the exact solution (optimal solution) and other progressive methods in the literature. These results are evaluated on the basis of their quality and time for its implementation. / O trabalho proposto nessa dissertação pertence à área de otimização combinatória, a qual visa encontrar uma solução para esses tipos de problema em um tempo computacional baixo e de forma eficaz. A otimização combinatória estuda um conjunto discreto de soluções, os quais possuem um número finito de elementos, para se poder encontrar a melhor solução viável para os problemas dessa grandeza. Uma das principais abordagens dessa área é o Problema do Caixeiro Viajante (PCV), principalmente devido à dimensão de possíveis soluções para o problema, fazendo com que seja intratável computacionalmente por métodos de buscas exaustivas. Face a todas essas características, este trabalho tem por objetivo estudar e desenvolver estratégias evolutivas para a resolução do Problema do Caixeiro Viajante com Demandas Heterogêneas (PCVDH), uma variação do PCV clássico. As estratégias evolutivas pertencem à classe da computação evolutiva, sendo métodos de busca inspirados na teoria da evolução das espécies, onde os melhores indivíduos competem pela sobrevivência. As estratégias evolutivas diferem das demais técnicas de otimização, pois a busca é realizada em uma população de soluções, não em um único ponto. Para a resolução do problema são propostos quatro algoritmos evolutivos, utilizando técnicas heurísticas e metaheurísticas para sua aplicação. Os resultados foram obtidos com testes utilizando instâncias de baixa densidade (baixa conexão), e comparados com a sua solução exata (solução ótima) e com outros métodos evolutivos encontrados na literatura. Esses resultados são avaliados com base na sua qualidade e tempo decorrido para sua execução.

For many real-world applications of evolutionary computation, the fitness function is obscured by random noise. This interferes with the evaluation and selection processes and adversely affects the performance of the algorithm. Noise can be effectively eliminated by averaging a large number of fitness samples for each candidate, but the number of samples used per candidate (the resampling rate) required to achieve this is usually prohibitively large and time-consuming. Hence there is a practical need for algorithms that handle noise without eliminating it. Moreover, the amount of noise (noise strength and distribution) may vary throughout the search space, further complicating matters. We study noisy problems for which the noise strength varies throughout the search space. Such problems have generally been ignored by previous work, which has instead generally focussed on the specific case where the noise strength is the same at all points in the search domain. However, this need not be the case, and indeed this assumption is false for many applications. For example, in games of chance such as Poker, some strategies may be more conservative than others and therefore less affected by the inherent noise of the game. This thesis makes three significant contributions in the field of noisy fitness functions: We present the concept of dynamic resampling. Dynamic resampling is a technique that varies the resampling rate based on the noise strength and fitness for each candidate individually. This technique is designed to exploit the variation in noise strength and fitness to yield a more efficient algorithm. We present several dynamic resampling algorithms and give results that show that dynamic resampling can perform significantly better than the standard resampling technique that is usually used by the optimisation community, and that dynamic resampling algorithms that vary their resampling rates based on both noise strength and fitness can perform better than algorithms that vary their resampling rate based on only one of the above. We study a specific class of noisy fitness functions for which we counterintuitively find that it is better to use a higher resampling rate in regions of lower noise strength, and vice versa. We investigate how the evolutionary search operates on such problems, explain why this is the case, and present a hypothesis (with supporting evidence) for classifying such problems. We present an adaptive engine that automatically tunes the noise compensation parameters of the search during the run, thereby eliminating the need for the user to choose these parameters ahead of time. This means that our techniques can be readily applied to real-world problems without requiring the user to have specialised domain knowledge of the problem that they wish to solve. These three major contributions present a significant addition to the body of knowledge for noisy fitness functions. Indeed, this thesis is the first work specifically to examine the implications of noise strength that varies throughout the search domain for a variety of noise landscapes, and thus starts to fill a large void in the literature on noisy fitness functions.

Dans ces travaux, nous nous concentrons sur l’amélioration de l’accessibilité web pourdes utilisateurs handicapés visuels à travers d’outils s’intègrant dans la plateforme SmartWeb Accessibility Platform (SWAP). Après une synthèse sur l’accessibilité, un panoramade SWAP est présenté. Notre première contribution concerne la compensation de la pertede contraste textuel dans les pages web pour les daltoniens tout en maintenant les intentionsde l’auteur retranscrites par les couleurs. Ce problème est modélisé sous la forme d’unproblème de minimisation d’une fonction numérique dépendante des couleurs initiales, desrelations entre les couleurs de la page et des couleurs modifiées. L’intérêt et l’efficacité detrois méthodes d’optimisation (mass-spring system, CMA-ES, API) ont été évaluées surdeux jeux de données (réel, artificiel). Notre deuxième contribution cherche à améliorer lastructure de pages web pour des utilisateurs de lecteurs d’écran afin de compenser la perted’information due à la linéarisation du contenu. Grâce à l’utilisation d’heuristiques et detechnique d’apprentissage, les zones caractéristiques de la page sont identifiées. La pageest complétée en utilisant des liens supplémentaires et les marqueurs ARIA afin de permettreune meilleure identification des zones par les lecteurs d’écran. Nos expérimentationsmontrent l’efficacité de notre proposition. / This thesis works are focused on enhancing web accessibility for users with visual disabilities using tools integrated within the SmartWeb Accessibility Platform (SWAP). After a synthesis on accessibility, SWAP is presented. Our first contribution consists in reducing the contrast loss for textual information in web pages for dichromat users while maintaining the author’s intentions conveyed by colors. The contrast compensation problem is reduced at minimizing a fitness function which depends on the original colors and the relationships between them. The interest and efficiency of three methods (mass-spring system, CMA-ES, API) are assessed on two datasets (real and artificial). The second contribution focuses on enhancing web page structure for screen reader users in order to overcome the effect of contents’linearization. Using heuristics and machine learning techniques, the main zones of the page are identified. The page structure can be enhanced using ARIA statements and access links to improve zone identification by screen readers.

Made available in DSpace on 2014-12-17T15:26:39Z (GMT). No. of bitstreams: 1 MariaJS_DISSERT.pdf: 2706687 bytes, checksum: 2f98eddad7bbc278c03ee45e4e226d95 (MD5) Previous issue date: 2013-04-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / This paper we study a random strategy called MOSES, which was introduced in 1996 by Fran?cois. Asymptotic results of this strategy; behavior of the stationary distributions of the chain associated to strategy, were derived by Fran?cois, in 1998, of the theory of Freidlin and Wentzell [8]. Detailings of these results are in this work. Moreover, we noted that an alternative approach the convergence of this strategy is possible without making use of theory of Freidlin and Wentzell, yielding the visit almost certain of the strategy to uniform populations which contain the minimum. Some simulations in Matlab are presented in this work / Neste trabalho estudamos uma estrat?gia aleat?ria chamada de MOSES, que foi introduzida por Fran?ois em 1996. Resultados assint?ticos desta estrat?gia; comportamento das distribui??es estacion?rias da cadeia associada a estrat?gia, foram derivados por Fran?ois, em 1998, da teoria de Freidlin e Wentzell [8]. Detalhamentos destes resultados est?o neste trabalho. Por outro lado, notamos que uma abordagem alternativa da converg?ncia desta estrat?gia ? poss?vel sem fazer uso da teoria de Freidlin e Wentzell, obtendo-se a visita quase certa da estrat?gia as popula??es uniformes que cont?m o m?m?nimo. Algumas simula??es no Matlab s?o apresentadas neste trabalho

In this thesis, an evolutionary system for searching quantum operators in the form of unitary matrices is implemented. The aim is to propose several representations of candidate solutions and settings of the evolutionary algorithm. Two evolutionary algorithms were applied: the genetic algorithm and evolutionary strategy. Furthermore, a method of generating a unitary matrix is presented which is used for the first time for this task. This method is in some aspects better than the previous ones. Finally, a comparison of all used techniques is shown in experiments.

Die vorliegende Dissertation befasst sich mit der theoretischen Vorhersage neuer Materialien. Ein evolutionärer Algorithmus, der zur Lösung dieses globalen Optimierungsproblems Konzepte der natürlichen Evolution imitiert, wurde entwickelt und ist als Programmpaket EVO frei verfügbar. EVO findet zuverlässig sowohl bekannte als auch neuartige Kristallstrukturen. Beispielsweise wurden die Strukturen von Germaniumnitrofluorid, einer neue Borschicht und mit dem gekreuzten Graphen einer bisher unbekannte Kohlenstoffstruktur gefunden. Ferner wurde in der Arbeit gezeigt, dass das reine Auffinden solcher Strukturen der erste Teil einer erfolgreichen Vorhersage ist. Weitere aufwendige Berechnungen sind nötig, die Aufschluss über die Stabilität der hypothetischen Struktur geben und Aussagen über zu erwartende Materialeigenschaften liefern.