This thesis deals with an optimization of waste collection in a mid-sized town. The model is formulated based on requirements from a real process. To deal with this problem, the original memetic algorithm was developed and implemented in C++.

This thesis deals with the problem of vehicle scheduling in public transport. It contains a theoretical introduction to vehicles scheduling and evolutionary algorithms. Vehicle scheduling is analyzed with respect to the bus timetables. Analysis of evolutionary algorithms is done with emphasis on the genetic algorithms and tabu-search method After the theoretical introduction, a memetic algorithm for the given problem is analyzed. Finally, the thesis contains a description of the optimization system implementation and discusses the experiments with the system.

O campo da Biometria abarca uma grande variedade de tecnologias usadas para identificar e verificar a identidade de uma pessoa por meio da mensuração e análise de vários aspectos físicos e/ou comportamentais do ser humano. Diversas modalidades biométricas têm sido propostas para reconhecimento de pessoas, como impressões digitais, íris, face e voz. Estas modalidades biométricas possuem características distintas em termos de desempenho, mensurabilidade e aceitabilidade. Uma questão a ser considerada com a aplicação de sistemas biométricos em mundo real é sua robustez a ataques por circunvenção, repetição e ofuscação. Esses ataques estão se tornando cada vez mais frequentes e questionamentos estão sendo levantados a respeito dos níveis de segurança que esta tecnologia pode oferecer. Recentemente, sinais biomédicos, como eletrocardiograma (ECG), eletroencefalograma (EEG) e eletromiograma (EMG) têm sido estudados para uso em problemas envolvendo reconhecimento biométrico. A formação do sinal do ECG é uma função da anatomia estrutural e funcional do coração e dos seus tecidos circundantes. Portanto, o ECG de um indivíduo exibe padrão cardíaco único e não pode ser facilmente forjado ou duplicado, o que tem motivado a sua utilização em sistemas de identificação. Entretanto, a quantidade de características que podem ser extraídas destes sinais é muito grande. A seleção de característica tem se tornado o foco de muitas pesquisas em áreas em que bases de dados formadas por dezenas ou centenas de milhares de características estão disponíveis. Seleção de característica ajuda na compreensão dos dados, reduzindo o custo computacional, reduzindo o efeito da maldição da dimensionalidade e melhorando o desempenho do preditor. O foco da seleção de característica é selecionar um subconjunto de característica a partir dos dados de entrada, que pode descrever de forma eficiente os dados de entrada ao mesmo tempo reduzir os efeitos de ruídos ou características irrelevantes e ainda proporcionar bons resultados de predição. O objetivo desta dissertação é analisar o impacto de algumas técnicas de seleção de característica tais como, Busca Gulosa, Seleção \\textit, Algoritmo Genético, Algoritmo Memético, Otimização por Enxame de Partículas sobre o desempenho alcançado pelos sistemas biométricos baseado em ECG. Os classificadores utilizados foram $k$-Vizinhos mais Próximos, Máquinas de Vetores Suporte, Floresta de Caminhos Ótimos e classificador baseado em distância mínima. Os resultados demonstram que existe um subconjunto de características extraídas do sinal de ECG capaz de fornecer altas taxas de reconhecimento / The field of biometrics includes a variety of technologies used to identify and verify the identity of a person by measuring and analyzing various physical and/or behavioral aspects of the human being. Several biometric modalities have been proposed for recognition of people, such as fingerprints, iris, face and speech. These biometric modalities have distinct characteristics in terms of performance, measurability and acceptability. One issue to be considered with the application of biometric systems in real world is its robustness to attacks by circumvention, spoof and obfuscation. These attacks are becoming more frequent and more questions are being raised about the levels of security that this technology can offer. Recently, biomedical signals, as electrocardiogram (ECG), electroencephalogram (EEG) and electromyogram (EMG) have been studied for use in problems involving biometric recognition. The ECG signal formation is a function of structural and functional anatomy of the heart and its surrounding tissues. Therefore, the ECG of an individual exhibits unique cardiac pattern and cannot be easily forged or duplicated, that have motivated its use in various identification systems. However, the amount of features that can be extracted from this signal is very large. The feature selection has become the focus of much research in areas where databases formed by tens or hundreds of thousands of features are available. Feature Selection helps in understanding data, reducing computation requirement, reducing the effect of curse of dimensionality and improving the predictor performance. The focus of feature selection is to select a subset of features from the input which can efficiently describe the input data while reducing effects from noise or irrelevant features and still provide good prediction results. The aim of this dissertation is to analyze the impact of some feature selection techniques, such as, greedy search, Backward Selection, Genetic Algorithm, Memetic Algorithm, Particle Swarm Optimization on the performance achieved by biometric systems based on ECG. The classifiers used were $k$-Nearest Neighbors, Support Vector Machines, Optimum-Path Forest and minimum distance classifier. The results demonstrate that there is a subset of features extracted from the ECG signal capable of providing high recognition rates

Submitted by Maicon Juliano Schmidt (maicons) on 2015-03-23T14:28:22Z No. of bitstreams: 1 Luan Carlos Nesi.pdf: 100100607 bytes, checksum: 6012e0f177d7b8f3807de72ff7d98315 (MD5) / Made available in DSpace on 2015-03-23T14:28:22Z (GMT). No. of bitstreams: 1 Luan Carlos Nesi.pdf: 100100607 bytes, checksum: 6012e0f177d7b8f3807de72ff7d98315 (MD5) Previous issue date: 2014-03-27 / Milton Valente / Nesse trabalho foi desenvolvido um modelo computacional de Hipermídia Adaptativa para geração de layouts de interface de aplicações. A pesquisa partiu de uma revisão sobre Hipermídia Adaptativa, com um apanhado sobre os conceitos e características dos métodos e técnicas de adaptação a fim de embasar seu desenvolvimento. Após, avaliou-se o uso das metaheurísticas Algoritmo Genético, Busca Tabu e Algoritmo Memético como as ferramentas de apoio no desenvolvimento do modelo. Na sequência, as Redes de Autômatos Estocásticos nortearam a modelagem do formalismo utilizado para a retenção de conhecimento. Dessas bases, foi desenvolvida a prova de conceito. Conseguinte, apresentam-se os experimentos realizados para validação. Os resultados obtidos pelo modelo foram de boa qualidade, indo ao encontro dos objetivos da pesquisa. Como decorrência deste trabalho, obteve-se um sistema capaz de gerar layouts, contemplando as características dos usuários e seus dispositivos, sendo capaz de acompanhar uma tendência de consumo de conteúdos não só mercadológica, mas também, social. / In this paper was developed a computational model of Adaptive Hypermedia for generation of interface layouts of applications. The research began with a review of Adaptive Hypermedia, with an overview of the concepts and characteristics of the methods and adaptation techniques in order to base its development. After, we evaluated the use of metaheuristic Genetic Algorithm, Tabu Search, and Memetic Algorithm as support tools in the development of the model. Following, the Stochastic Automata Networks guided the modeling of the formalism used for knowledge retention. These bases, the proof of concept were developed. Therefore, we present the experiments to validate. The obtained results by the model were of good quality, meeting the research objectives. As results of this work, we obtained a system capable to generate layouts, considering the characteristics of the users and their devices, being able to follow a trend of content consumption not only marketing, but also social.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-06-16T12:13:46Z No. of bitstreams: 2 Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) / Made available in DSpace on 2017-06-16T12:13:47Z (GMT). No. of bitstreams: 2 Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Previous issue date: 2017-03-23 / CNPQ – Conselho Nacional de Desenvolvimento Científico e Tecnológico / A contribuição de metaheurísticas, em especial a dos algoritmos evolutivos, na área de otimização combinatória é de extrema relevância, pois auxiliam na busca de soluções próximas ao ótimo para problemas complexos da vida real cuja resolução em tempo aceitável é inviável devido a sua complexidade computacional, oferecendo uma flexibilidade importante na modelagem do problema. Este trabalho se propõe a apresentar e implementar um modelo computacional a ser utilizado na otimização do escalonamento de ordens de produção utilizando um Algoritmo Memético (AM), que permite a busca tanto da melhor sequência das ordens de produção quanto dos lotes de tamanho variável em que a quantidade de cada operação pode ser subdividida. A possibilidade de utilização de máquinas alternativas, de recursos secundários, de intervalos de indisponibilidade e de lotes de transferência, é apresentada no modelo, o que lhe proporciona grande robustez e aplicabilidade em ambientes de manufatura flexível, permitindo uma modelagem do Flexible Job Shop Scheduling Problem (FJSSP) que reflete com maior fidedignidade a realidade do ambiente fabril, gerando como resultado um escalonamento otimizado e aderente às necessidades da fábrica. Várias instâncias do FJSSP são utilizadas nos testes e os resultados obtidos comprovam que o algoritmo proposto consegue otimizar o escalonamento das ordens de produção de cada instância de maneira eficiente. / The contribution of meta-heuristics, especially evolutionary algorithms, in combinatorial optimization area is extremely important, as they help in finding near optimal solutions to complex real-life problems whose resolution is infeasible in acceptable time due to its computational complexity, offering an important flexibility in the modeling of problem. This study propose to present and implement a computational model to be used in optimizing the production scheduling of manufacturing orders using a Memetic Algorithm that allows to search both the best sequence of jobs as of variable size batches that the quantity of each operation can be subdivided. The possibility of using alternative resources, operations with secondary resources, unavailability intervals and batch transfer lots are features presented in the model, which lends it great robustness and applicability to flexible manufacturing environments, allowing the modeling of Flexible Job Shop Scheduling Problem (FJSSP) that reflects with higher accuracy the real manufacturing environment, generating optimized scheduling results that are adhering to the plant needs. Multiple instances of FJSSP are used in the tests and the results show that the proposed algorithm succeeds in optimizing the scheduling of production orders for each instance so efficient.

Submitted by Liliane Ferreira (ljuvencia30@gmail.com) on 2018-04-04T11:42:16Z No. of bitstreams: 2 Dissertação - Caio José Fernandes Póvoa - 2018.pdf: 6079271 bytes, checksum: 5efa21665d3c5f3bf6b4a58652fff6b4 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-04-04T13:23:10Z (GMT) No. of bitstreams: 2 Dissertação - Caio José Fernandes Póvoa - 2018.pdf: 6079271 bytes, checksum: 5efa21665d3c5f3bf6b4a58652fff6b4 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-04-04T13:23:10Z (GMT). No. of bitstreams: 2 Dissertação - Caio José Fernandes Póvoa - 2018.pdf: 6079271 bytes, checksum: 5efa21665d3c5f3bf6b4a58652fff6b4 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-03-22 / This dissertation of master degree describes methods of optimizing routes and allocating towers of overhead power lines, with the objective of meeting technical, structural and constructive constraints, and reducing financial costs. The generated solutions are graphically presented through the transmission line profile and its 3-dimension representation upon the elevation map of the area. For the projects evaluation, elements of structural analysis are used, highlighting the Matrix Structural Analysis for the study of efforts and deformations in the towers and their components. Three methods are proposed, each one using different approaches. First, it will be shown an optimization algorithm based on Evolutionary Computation, characterized by the application of natural selection on individuals generated from mutations and genetic crossover. The second algorithm was inspired by the well-known Nelder-Mead optimization method. The triangular transformations addressed in the original method were adapted and physically implemented to transmission lines. The last optimization algorithm presented is a hybridization of the two previous methods. Finally, a performance comparison of the algorithms, in which each one of them will be applied to three different cases, will be carried out in order to validate them. / Esta dissertação de mestrado descreve métodos de otimização de trajetos e alocação de torres de linhas aéreas de transmissão de energia elétrica, com o objetivo de obedecer a restrições técnicas, estruturais e construtivas, e de reduzir custos financeiros. As soluções encontradas são apresentadas graficamente a partir da plotagem do perfil da linha de transmissão, e da sua representação em três dimensões sobre o mapa de relevo da região. Para a avaliação dos projetos, utilizam-se elementos de análise estrutural, destacando-se a Análise Estrutural Matricial para o estudo dos esforços e deformações nas torres e seus componentes. São propostos três métodos que utilizam abordagens diferentes. Primeiramente, será considerado um algoritmo de otimização baseado na Computação Evolucionária, caracterizando-se pela aplicação da seleção natural ao longo de gerações, em indivíduos gerados a partir de mutações e recombinações. O segundo algoritmo apresentado é inspirado no consagrado método de Nelder-Mead, sendo as transformações triangulares, por ele apresentadas, adaptadas e implementadas fisicamente a linhas de transmissão. O último método de otimização é uma hibridação dos dois métodos anteriores. Por fim, será feita uma comparação de desempenho dos algoritmos apresentados, a partir da aplicação de cada um deles a três estudos de caso distintos para validá-los.

O campo da Biometria abarca uma grande variedade de tecnologias usadas para identificar e verificar a identidade de uma pessoa por meio da mensuração e análise de vários aspectos físicos e/ou comportamentais do ser humano. Diversas modalidades biométricas têm sido propostas para reconhecimento de pessoas, como impressões digitais, íris, face e voz. Estas modalidades biométricas possuem características distintas em termos de desempenho, mensurabilidade e aceitabilidade. Uma questão a ser considerada com a aplicação de sistemas biométricos em mundo real é sua robustez a ataques por circunvenção, repetição e ofuscação. Esses ataques estão se tornando cada vez mais frequentes e questionamentos estão sendo levantados a respeito dos níveis de segurança que esta tecnologia pode oferecer. Recentemente, sinais biomédicos, como eletrocardiograma (ECG), eletroencefalograma (EEG) e eletromiograma (EMG) têm sido estudados para uso em problemas envolvendo reconhecimento biométrico. A formação do sinal do ECG é uma função da anatomia estrutural e funcional do coração e dos seus tecidos circundantes. Portanto, o ECG de um indivíduo exibe padrão cardíaco único e não pode ser facilmente forjado ou duplicado, o que tem motivado a sua utilização em sistemas de identificação. Entretanto, a quantidade de características que podem ser extraídas destes sinais é muito grande. A seleção de característica tem se tornado o foco de muitas pesquisas em áreas em que bases de dados formadas por dezenas ou centenas de milhares de características estão disponíveis. Seleção de característica ajuda na compreensão dos dados, reduzindo o custo computacional, reduzindo o efeito da maldição da dimensionalidade e melhorando o desempenho do preditor. O foco da seleção de característica é selecionar um subconjunto de característica a partir dos dados de entrada, que pode descrever de forma eficiente os dados de entrada ao mesmo tempo reduzir os efeitos de ruídos ou características irrelevantes e ainda proporcionar bons resultados de predição. O objetivo desta dissertação é analisar o impacto de algumas técnicas de seleção de característica tais como, Busca Gulosa, Seleção \\textit, Algoritmo Genético, Algoritmo Memético, Otimização por Enxame de Partículas sobre o desempenho alcançado pelos sistemas biométricos baseado em ECG. Os classificadores utilizados foram $k$-Vizinhos mais Próximos, Máquinas de Vetores Suporte, Floresta de Caminhos Ótimos e classificador baseado em distância mínima. Os resultados demonstram que existe um subconjunto de características extraídas do sinal de ECG capaz de fornecer altas taxas de reconhecimento / The field of biometrics includes a variety of technologies used to identify and verify the identity of a person by measuring and analyzing various physical and/or behavioral aspects of the human being. Several biometric modalities have been proposed for recognition of people, such as fingerprints, iris, face and speech. These biometric modalities have distinct characteristics in terms of performance, measurability and acceptability. One issue to be considered with the application of biometric systems in real world is its robustness to attacks by circumvention, spoof and obfuscation. These attacks are becoming more frequent and more questions are being raised about the levels of security that this technology can offer. Recently, biomedical signals, as electrocardiogram (ECG), electroencephalogram (EEG) and electromyogram (EMG) have been studied for use in problems involving biometric recognition. The ECG signal formation is a function of structural and functional anatomy of the heart and its surrounding tissues. Therefore, the ECG of an individual exhibits unique cardiac pattern and cannot be easily forged or duplicated, that have motivated its use in various identification systems. However, the amount of features that can be extracted from this signal is very large. The feature selection has become the focus of much research in areas where databases formed by tens or hundreds of thousands of features are available. Feature Selection helps in understanding data, reducing computation requirement, reducing the effect of curse of dimensionality and improving the predictor performance. The focus of feature selection is to select a subset of features from the input which can efficiently describe the input data while reducing effects from noise or irrelevant features and still provide good prediction results. The aim of this dissertation is to analyze the impact of some feature selection techniques, such as, greedy search, Backward Selection, Genetic Algorithm, Memetic Algorithm, Particle Swarm Optimization on the performance achieved by biometric systems based on ECG. The classifiers used were $k$-Nearest Neighbors, Support Vector Machines, Optimum-Path Forest and minimum distance classifier. The results demonstrate that there is a subset of features extracted from the ECG signal capable of providing high recognition rates

L'autopartage est un service de mobilité qui offre les mêmes avantages que les voitures particulières mais sansnotion de propriété. Les clients du système peuvent accéder aux véhicules sans ou avec réservation préalable. Laflotte de voitures est distribuée entre les stations et les clients peuvent prendre une voiture d'une station et ladéposer dans n'importe quelle autre station (one-way), chaque station disposant d'un nombre maximum de placesde stationnement. La demande pour la prise ou le retour des voitures dans chaque station est souvent asymétriqueentre les stations et varie au cours de la journée. Par conséquent, certaines stations accumulent des voitures etatteignent leur capacité maximale prévenant alors de nouvelles voitures de trouver une place de stationnement.Dans le même temps, des stations se vident et conduisent au rejet de la demande de retrait de clients. Notre travailporte sur l'optimisation des opérations de redéploiement de voitures afin de redistribuer efficacement les voitures surles stations suivant la demande qui varie en fonction du temps et de l'espace. Dans les systèmes d'autopartage àsens unique, le problème du redéploiement de voitures sur les stations est techniquement plus difficile que leproblème de la redistribution des vélos dans les systèmes de vélopartage. Dans ce dernier, on peut utiliser uncamion pour déplacer plusieurs vélos en même temps, alors que nous ne pouvons pas le faire dans le systèmeautopartage en raison de la taille des voitures et de la difficulté de chargement et de déchargement. Ces opérationsaugmentent le coût de fonctionnement du système d'autopartage sur l'opérateur. De ce fait, l'optimisation de cesopérations est essentielle afin de réduire leur coût. Dans cette thèse, nous développons un modèle deprogrammation linéaire en nombre entier pour ce problème. Ensuite, nous présentons trois politiques différentes deredéploiement de voitures que nous mettons en oeuvre dans des algorithmes de recherche gloutonne et nousmontrons que les opérations de redéploiement qui ne considèrent pas les futures demandes ne sont pas efficacesdans la réduction du nombre de demandes rejetées. Les solutions fournies par notre algorithme glouton sontperformantes en temps d'exécution (moins d'une seconde) et en qualité en comparaison avec les solutions fourniespar CPLEX. L'évaluation de la robustesse des deux approches présentées par l'ajout d'un bruit stochastique sur lesdonnées d'entrée montre qu'elles sont très dépendantes des données même avec l'adoption de valeur de seuil deredéploiement. En parallèle à ce travail algorithmique, l'analyse de variance (ANOVA) et des méthodes derégression multilinéaires ont été appliqués sur l'ensemble de données utilisées pour construire un modèle global afind'estimer le nombre de demandes rejetées. Enfin, nous avons développé et comparé deux algorithmesévolutionnaires multicritères pour prendre en compte l'indécision sur les objectifs de l'optimisation, NSGA-II et unalgorithme mémétique qui a montré une bonne performance pour résoudre ce problème. / To buy it. Users can have access to vehicles on the go with or without reservation. Each station has a maximumnumber of parking places. In one-way carsharing system, users can pick up a car from a station and drop it in anyother station. The number of available cars in each station will vary based on the departure and the arrival of cars oneach station at each time of the day. The demand for taking or returning cars in each station is often asymmetric andis fluctuating during the day. Therefore, some stations will accumulate cars and will reach their maximum capacitypreventing new arriving cars from finding a parking place, while other stations will become empty which lead to therejection of new users demand to take a car. Users expect that cars are always available in stations when they needit, and they expect to find a free parking place at the destination station when they want to return the rented car aswell. However, maintaining this level of service is not an easy task. For this sake, carsharing operators recruitemployees to relocate cars between the stations in order to satisfy the users' demands.Our work concerns the optimization of the car relocation operations in order to efficiently redistribute the cars overthe stations with regard to user demands, which are time and space dependent. In one-way carsharing systems, therelocation problem is technically more difficult than the relocation problem in bikesharing systems. In the latter, wecan use trucks to move several bikes at the same time, while we cannot do this in carsharing system because of thesize of cars and the difficulty of loading and unloading cars. These operations increase the cost of operating thecarsharing system.As a result, optimizing these operations is crucial in order to reduce the cost of the operator. In this thesis, we modelthis problem as an Integer Linear Programming model. Then we present three different car relocation policies thatwe implement in a greedy search algorithm. The comparison between the three policies shows that car relocationoperations that do not consider future demands are not effective in reducing the number of rejected demands.Results prove that solutions provided by our greedy algorithm when using a good policy, are competitive withCPLEX solutions. Furthermore, adding stochastic modification on the input data proves that the robustness of thetwo presented approaches to solve the relocation problem is highly dependent on the input demand even afteradding threshold values constraints. After that, the analysis of variance (ANOVA) and the multi-linear regressionmethods were applied on the used dataset in order to build a global model to estimate the number of rejecteddemands. Finally, we developed and compared two multi-objectives evolutionary algorithms to deal with thedecisional aspect of the car relocation problem using NSGA-II and memetic algorithms.

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

Το κύριο στοιχείο της διατριβής είναι οι Εξελικτικοί Αλγόριθμοι. Στο πρώτο μέρος παρουσιάζονται οι Μιμιδικοί Αλγόριθμοι. Οι Μιμιδικοί Αλγόριθμοι είναι υβριδικά σχήματα που συνδυάζουν τους Εξελιτκικούς Αλγορίθμους με μεθόδους τοπικής αναζήτησης. Οι Μιμιδικοί Αλγόριθμοι συγκρίθηκαν με τους Εξελικτικούς Αλγορίθμους σε πληθώρα προβλημάτων ολικής βελτιστοποίησης και είχαν καλύτερα αποτελέσματα. Στο δεύτερο μέρος μελετήθηκαν προβλήματα μη γραμμικής δυναμικής. Αυτά ήταν η εκτίμηση της περιοχής ευστάθειας διατηρητικών απεικονίσεων, η ανίχνευση συντονισμών και ο υπολογισμός περιοδικών τροχιών. Τα αποτελέσματα ήταν ικανοποιητικά. / The main objective of the thesis was the study of Evolutionary Algorithms. At the first part, Memetic Algorithms were introduced. Memetic Algorithms are hybrid schemes that combine Evolutionary Algorithms and local search methods. Memetic Algorithms were compared to Evolutionary Algorithms in various problems of global optimization and they had better performance. At the second part, problems from nonlinear dynamics were studied. These were the estimation of the stability region of conservative maps, the detection of resonances and the computation of periodic orbits. The results were satisfactory.