Typically, systems biology focuses on the form and function of networks of biochemical interactions. Questions inevitably arise as to the evolutionary origin of those networks' properties. Such questions are of interest to a growing number of systems biologists, and several groups have published studies shown how varying environments can affect network topology and lead to increased evolvability. For decades, evolutionary biologists have also investigated the evolution of evolvability and its relationship to the interactions between genotype and phenotype. While the perspectives of systems and evolutionary biologists sometimes differ, their interests in patterns of interactions and evolvability have much in common. This thesis attempts to bring together the perspectives of systems and evolutionary theory to investigate the evolutionary effects of fluctuating environments. Chapter 1 introduces the necessary themes, terminology and literature from these fields. Chapter 2 explores how rapid environmental fluctuations, or "noise", affects network size and robustness. In Chapter 3, we use the Avida platform to investigate the relationship between genetic architecture, fluctuating environments and population biology. Chapter 4 examines contingency loci as a physical basis for evolvability, while chapter 5 presents a 500-generation laboratory evolution experiment which exposes E. coli to varying environments. The final discussion, concludes that the evolution of generalism can lead to genetic architectures which confer evolvability, which may arise in rapidly fluctuating environments as a by-product of generalism rather than as a selected trait.

Adjustment of a given age distribution to a desired age distribution within a required time frame is dynamically performed for the purpose of Human Resource (HR) planning in Human Resource Management (HRM). The adjustment process is carried out by adding the adjustment magnitudes to the existing number of employees at the selected age groups on the yearly basis. A model of a discrete dynamical system is employed to emulate the evolution of the age distribution used under the adjustment process. Genetic Algorithms (GA) is applied for determining the adjustment magnitudes that influence the dynamics of the system. An interesting aspect of the problem lies in the high number of constraints; though the constraints are fundamental, they are considerably higher in number than in many other optimization problems. An adaptive penalty scheme is proposed for handling the constraints. Numerical examples show that GA with the utilized adaptive penalty scheme provides potential means for HR planning in HRM.

Efficient production scheduling is important for both maximizing productivity and minimizing costs in manufacturing environments. This thesis presents an approach to optimizing production scheduling using Artificial Intelligence (AI) and Genetic Algorithms (GAs). The primary goal is to develop a generalized solution that can be modified and adapted to the varying needs that different production- or manufacturing lines may have. This research has two main research questions that address the problem at hand. (1) Can Genetic Algorithms be used to optimize a sequence of products in a production line? and (2) How effectively can Genetic Algorithms optimize the sequencing of production tasks in diverse production lines to minimize total order processing times? Through experimentation with various GA configurations the results achieved suggested that GAs were appropriate for sequence optimization. The study demonstrates that GAs can optimize a production line up to almost 42% , which significantly reduced the total processing time. The thesis also highlights the importance of the representation of data, which is essential for the optimization of the sequence.

The aim of this study is to analysis, investigate and find out the solutions for the problems associated with the implementations of antennas RFID Reader and Tag for various applications. In particular, the efficiency of the RFID reader antenna and the detection range of the RFID tag antenna, subject to a small and compact antenna's design configuration have been studied. The present work has been addressed directly to reduce the cost, size and increase the detection range and communication reliability of the RFID framework antennas. Furthermore, the modelling concept of RFID passive tags mounted on various materials including the novel design of RFID reader antenna using Genetic Algorithm (GA) are considered and discussed to maintain reliable and efficient antenna radiation performances. The main benefit of applying GA is to provide fast, accurate and reliable solutions of antenna's structure. Therefore, the GA has been successfully employed to design examples: meander-line, two linear cross elements and compact Helical- Spiral antennas. In addition, a hybrid method to model the human body interaction with RFID tag antenna operating at 900MHz has been studied. The near field distribution and the radiation pattern together with the statistical distribution of the radiation efficiency and the absorbed power in terms of cumulative distribution functions for different orientation and location of RFID's tag antenna on the human body have been demonstrated. Several tag antennas wi th symmetrical and unsymmetrical structure configurations operating in the European UHF band 850-950 MHz have been fabricated and tested. . The measured and simulated results have been found to be in a good agreement with reasonable impedance matching to the typical input impedance of an RFID integrated circuit chip and nominal power gain and radiation patterns.

Submitted by Automação e Estatística (sst@bczm.ufrn.br) on 2018-04-11T20:06:38Z No. of bitstreams: 1 BrunoJacomeCavalcanti_TESE.pdf: 5397909 bytes, checksum: 5a245eec570a69adf8ca5d791aaddf70 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2018-04-16T20:46:19Z (GMT) No. of bitstreams: 1 BrunoJacomeCavalcanti_TESE.pdf: 5397909 bytes, checksum: 5a245eec570a69adf8ca5d791aaddf70 (MD5) / Made available in DSpace on 2018-04-16T20:46:19Z (GMT). No. of bitstreams: 1 BrunoJacomeCavalcanti_TESE.pdf: 5397909 bytes, checksum: 5a245eec570a69adf8ca5d791aaddf70 (MD5) Previous issue date: 2017-10-20 / A perfeita funcionalidade dos sistemas de comunicações de 3ª. e 4ª. gerações requerem, entre outras coisas, do conhecimento dos valores numéricos da predição das perdas de propagação dos sinais propagantes nos ambientes urbano, suburbano e rural. Portanto, o estudo das condições de propagação em um ambiente qualquer sempre será uma preocupação dos engenheiros projetistas. A análise e desenvolvimento de modelos robustos de predição de perdas de propagação em redes de comunicações Long Term Evolution (LTE) e Long Term Evolution Advanced (LTE-A) usando técnicas de Inteligência Artificial são realizadas neste trabalho. Os procedimentos metodológicos empregados foram aplicados no melhoramento da predição dos modelos de perda de propagação empíricos SUI, ECC-33, Ericsson 9999, TR 36.942 e o modelo do Espaço Livre, aplicados em redes LTE e LTE-A nas frequências de 800 MHz, 1800 MHz e 2600 MHz, para ambientes suburbanos em cidades de porte médio do nordeste do Brasil. Assim, nesta tese propõem-se dois modelos de Redes Neurais Artificiais (RNA): (i) o modelo de rede neural com entradas baseadas em erro (RNBE), utilizando como principal alimentador da rede o erro entre dados medidos e simulados, e, (ii) o modelo de rede neural com entradas baseadas no terreno (RNBT). O desempenho desses modelos foram comparados com os modelos de propagação considerados no trabalho e também as versões otimizadas utilizando Algoritmos Genéticos (AG) e o Método dos Mínimos Quadrados (LMS). Também foram realizadas comparações com valores medidos, obtidos a partir de uma campanha de medição realizada na cidade de Natal, Estado do Rio Grande do Norte. Os resultados finais obtidos através de simulações e medições apresentaram boas concordâncias métricas, com destaque para a performance do modelo RNBE. A principal contribuição dessa tese é que, ao utilizar essas técnicas que fazem uso de maneira mais eficiente dos modelos de propagação empíricos, pode-se estimar sinais propagantes realistas, evitando erros no planejamento e implementações de redes sem fio LTE e LTE-A em áreas suburbanas. / The perfect functionality of the 3rd and 4th generation of wireless systems requires, among other parameters, knowledge of the numerical values of the prediction of loss of propagation of propagation signals in urban, suburban and rural environments. Therefore, the study of propagation conditions in any environment will always be a concern of design engineers. The analysis and development of robust propagation loss prediction models in Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A) communications networks using Artificial Intelligence techniques is performed in this work. The methodologies used were applied to improve the prediction of loss of empirical propagation SUI, ECC-33, Ericsson 9999, TR 36.942 models and the Free Space model applied in LTE and LTE-A networks in the frequencies of 800 MHz, 1800 MHz and 2600 MHz, for suburban environments in mid-sized cities in northeastern Brazil. Thus, in these thesis two models of Artificial Neural Networks (RNA) are proposed: (i) the neural network model with inputs based on error (RNBE) using as main feeder of the network the error between measured and simulated data, and (ii) the neural network model with land-based inputs (RNBT). The performance of these models was compared with the models of propagation considered in the work and also the versions optimized using Genetic Algorithms (AG) and the Least Square Method (LMS). Comparisons were also made with measured values, obtained from a measurement campaign carried out in the city of Natal, state of Rio Grande do Norte. The final results obtained through simulations and measurements presented good metric concordances, with emphasis on the performance of the RNBE model. Thus, the main contribution of this thesis is that, by using these techniques that make more efficient use of empirical propagation models, we can estimate realistic propagation signals, avoiding errors in the planning and implementations of LTE and LTE- A wireless networks in suburban areas.

The aim of this study is to analysis, investigate and find out the solutions for the problems associated with the implementations of antennas RFID Reader and Tag for various applications. In particular, the efficiency of the RFID reader antenna and the detection range of the RFID tag antenna, subject to a small and compact antenna¿s design configuration have been studied. The present work has been addressed directly to reduce the cost, size and increase the detection range and communication reliability of the RFID framework antennas. Furthermore, the modelling concept of RFID passive tags mounted on various materials including the novel design of RFID reader antenna using Genetic Algorithm (GA) are considered and discussed to maintain reliable and efficient antenna radiation performances. The main benefit of applying GA is to provide fast, accurate and reliable solutions of antenna¿s structure. Therefore, the GA has been successfully employed to design examples: meander-line, two linear cross elements and compact Helical- Spiral antennas. In addition, a hybrid method to model the human body interaction with RFID tag antenna operating at 900MHz has been studied. The near field distribution and the radiation pattern together with the statistical distribution of the radiation efficiency and the absorbed power in terms of cumulative distribution functions for different orientation and location of RFID¿s tag antenna on the human body have been demonstrated. Several tag antennas wi th symmetrical and unsymmetrical structure configurations operating in the European UHF band 850-950 MHz have been fabricated and tested. . The measured and simulated results have been found to be in a good agreement with reasonable impedance matching to the typical input impedance of an RFID integrated circuit chip and nominal power gain and radiation patterns.

The objectives of this research work are to investigate, design and implement circularly-polarized antennas to be used for handheld satellite mobile communication and wireless sensor networks. Several antennas such as Quadrifilar Spiral Antennas (QSAs), two arm Square Spiral and stripline or coaxially-fed microstrip patch antennas are developed and tested. These antennas are investigated and discussed to operate at L band (1.61-1.6214GHz) and ISM band (2.4835-2.5GHz) A substantial size reduction was achieved compared to conventional designs by introducing special modifications to the antenna geometries. Most of the antennas are designed to produce circularly-polarized broadside-beam except for wireless sensor network application a circularly-polarized conical-beam is considered. The polarization purity and Specific Absorption Rate (SAR) of two dual-band antennas for satellite-mobile handsets next to the human head are investigated and discussed, using a hybrid computational method. A small distance between the head and the handset is chosen to highlight the effects of the relatively high-radiated power proposed from this particular antenna. A Genetic Algorithm in cooperation with an electromagnetic simulator has been introduced to provide fast, accurate and reliable solutions for antenna design structures. Circularly-polarized quadrifilar helical antenna handset and two air-dielectric microstrip antennas were studied. The capabilities of GA are shown as an efficient optimisation tool for selecting globally optimal parameters to be used in simulations with an electromagnetic antenna design code, seeking convergence to designated specifications. The results in terms of the antenna size and radiation performance are addressed, and compared to measurements and previously published data.

The objectives of this research work are to investigate, design and implement circularly-polarized antennas to be used for handheld satellite mobile communication and wireless sensor networks. Several antennas such as Quadrifilar Spiral Antennas (QSAs), two arm Square Spiral and stripline or coaxially-fed microstrip patch antennas are developed and tested. These antennas are investigated and discussed to operate at L band (1.61-1.6214GHz) and ISM band (2.4835-2.5GHz) A substantial size reduction was achieved compared to conventional designs by introducing special modifications to the antenna geometries. Most of the antennas are designed to produce circularly-polarized broadside-beam except for wireless sensor network application a circularly-polarized conical-beam is considered. The polarization purity and Specific Absorption Rate (SAR) of two dual-band antennas for satellite-mobile handsets next to the human head are investigated and discussed, using a hybrid computational method. A small distance between the head and the handset is chosen to highlight the effects of the relatively high-radiated power proposed from this particular antenna. A Genetic Algorithm in cooperation with an electromagnetic simulator has been introduced to provide fast, accurate and reliable solutions for antenna design structures. Circularly-polarized quadrifilar helical antenna handset and two air-dielectric microstrip antennas were studied. The capabilities of GA are shown as an efficient optimisation tool for selecting globally optimal parameters to be used in simulations with an electromagnetic antenna design code, seeking convergence to designated specifications. The results in terms of the antenna size and radiation performance are addressed, and compared to measurements and previously published data.

Šiame baigiamajame darbe optimizuojama plokščiojo kūno forma esant žinomai apkrovai ir kraštinėms sąlygoms. Aptariami pagrindiniai optimizavimo tipai, globaliojo optimizavimo ir kontinuumo diskretizavimo metodai. Forma optimizuojama trimis skirtingais neadaptyviais genetiniais algoritmais ir trimis adaptyviais, taikant migracijos strategijas. Poslinkiai ir von Mises įtempimai skaičiuojami baigtinių elementų metodu. Darbo užduotis realizuota C++ kalba. Darbą sudaro 9 dalys: įvadas, kūno formos modeliavimo metodų apžvalga, baigtinių elementų metodas, genetiniai algoritmai, kūno formos optimizavimo uždavinys, rezultatų palyginimas, išvados, literatūros sąrašas, priedai. Darbo apimtis - 62 p. teksto be priedų, 39 iliustr., 5 lent., 24 bibliografinių šaltinių. / The task of Master thesis is to optimize shape of plane body with given loading and boundary conditions. The classes of structural optimization problems, the methods of global optimization and discretization of continual structures are discussed. For shape optimization three different not adaptive and three adaptive genetic algorithms with migration strategy are used, displacements and von Mises stresses are calculated using finite element method. The program was created using C++ language. The work consists of 9 parts: introduction, overview of methods used for shape optimization, finite element method, genetic algorithms, shape optimization of plane body, results, conclusions, references, appendixes. Work consist of 62 p. text without appendixes, 39 pictures, 5 tables, 24 bibliographical entries. Appendixes are included.