The main focus of this thesis are hypercubes. In the first part, we introduce hypercubes, which form an interesting class of graphs that has practical uses in networks and distributed computing. Because of their varied applications, the thesis describes the graph-theory problems related to hypercubes such as searching for detour spanners, minimizing their maximal degree and finding multiple edge- disjoint spanners. It also overviews current results on selected hypercube problems and proposes a solution using a genetic algorithm. The genetic algorithm is designed, implemented and its performance is evaluated. The conclusion is that applying a genetic algorithm to some hypercube problems is a viable, but not the most effective method.

Stricter regulations and evolving environmental concerns have been exerting ever-increasing pressure on the automotive industry to produce low carbon vehicles that reduce emissions. As a result, increasing numbers of alternative powertrain architectures have been released into the marketplace to address this need. However, with a myriad of possible alternative powertrain configurations, which is the most appropriate type for a given vehicle class and duty cycle? To that end, comparative analyses of powertrain configurations have been widely carried out in literature; though such analyses only considered limited types of powertrain architectures at a time. Collating the results from these literature often produced findings that were discontinuous, which made it difficult for drawing conclusions when comparing multiple types of powertrains. The aim of this research is to propose a novel methodology that can be used by practitioners to improve the methods for comparative analyses of different types of powertrain architectures. Contrary to what has been done so far, the proposed methodology combines an optimisation algorithm with a Modular Powertrain Structure that facilitates the simultaneous approach to optimising multiple types of powertrain architectures. The contribution to science is two-folds; presenting a methodology to simultaneously select a powertrain architecture and optimise its component sizes for a given cost function, and demonstrating the use of multi-objective optimisation for identifying trade-offs between cost functions by powertrain architecture selection. Based on the results, the sizing of the powertrain components were influenced by the power and energy requirements of the drivecycle, whereas the powertrain architecture selection was mainly driven by the autonomy range requirements, vehicle mass constraints, CO2 emissions, and powertrain costs. For multi-objective optimisation, the creation of a 3-dimentional Pareto front showed multiple solution points for the different powertrain architectures, which was inherent from the ability of the methodology to concurrently evaluate those architectures. A diverging trend was observed on this front with the increase in the autonomy range, driven primarily by variation in powertrain cost per kilometre. Additionally, there appeared to be a trade-off in terms of electric powertrain sizing between CO2 emissions and lowest mass. This was more evident at lower autonomy ranges, where the battery efficiency was a deciding factor for CO2 emissions. The results have demonstrated the contribution of the proposed methodology in the area of multi-objective powertrain architecture optimisation, thus addressing the aims of this research.

The present work investigates the applicability of Genetic Algorithms (GA) to the problem of signal propagation in Native Neuronal Networks (NNNs). These networks are comprised of neurons, some of which receive input signals. The signals propagate though the network by transmission between neurons. The research focuses on the regeneration of the output signal of the network without knowing the original input signal. The computational complexity of the problem is prohibitive for the exact computation. We propose to use a heuristic approach called Genetic Algorithm. Three algorithms are developed, based on the GA technique. The developed algorithms are tested on two different networks with varying input signals. The results obtained from the testing indicate significantly better performance of the developed algorithms compared to the Uniform Random Search (URS) technique, which is used as a control group. The importance of the research is in the demonstration of the ability of GA-based algorithms to successfully solve the problem at hand.

In this thesis a methodology is presented to optimise non–linear mathematical models in numerical engineering applications. The method is based on biological evolution and uses known concepts of genetic algorithms and evolutionary compu- tation. The working principle is explained in detail, the implementation is outlined and alternative approaches are mentioned. The optimisation is then tested on a series of benchmark cases to prove its validity. It is then applied to two different types of problems in computational engineering. The first application is the mathematical modeling of turbulence. An overview of existing turbulence models is followed by a series of tests of different models applied to various types of flows. In this thesis the optimisation method is used to find improved coefficient values for the k–ε, the k–ω-SST and the Spalart–Allmaras models. In a second application optimisation is used to improve the quality of a computational mesh automatically generated by a third party software tool. This generation can be controlled by a set of parameters, which are subject to the optimisation. The results obtained in this work show an improvement when compared to non–optimised results. While computationally expensive, the genetic optimisation method can still be used in engineering applications to tune predefined settings with the aim to produce results of higher quality. The implementation is modular and allows for further extensions and modifications for future applications.

The purpose of this thesis was to identify the optimal design parameters for a jet nozzle which obtains a local maximum shear stress while maximizing the average shear stress on the floor of a fluid filled system. This research examined how geometric parameters of a jet nozzle, such as the nozzle's angle, height, and orifice, influence the shear stress created on the bottom surface of a tank. Simulations were run using a Computational Fluid Dynamics (CFD) software package to determine shear stress values for a parameterized geometric domain including the jet nozzle. A response surface was created based on the shear stress values obtained from 112 simulated designs. A multi-objective optimization software utilized the response surface to generate designs with the best combination of parameters to achieve maximum shear stress and maximum average shear stress. The optimal configuration of parameters achieved larger shear stress values over a commercially available design.

Segundo uma pesquisa recente realizada junto ao setor de fundições, uma importante preocupação do setor é melhorar seu planejamento de produção. Um plano de produção em uma fundição envolve duas etapas interdependentes: a determinação das ligas a serem fundidas e dos lotes que serão produzidos. Neste trabalho, estudamos o problema de dimensionamento de lotes para fundições de pequeno porte, cujo objetivo é determinar um plano de produção de mínimo custo. Como sugerido na literatura, a heurística proposta trata as etapas do problema de forma hierárquica: inicialmente são definidas as ligas e, posteriormente, os lotes que são produzidos a partir delas. Para a solução do problema, propomos um algoritmo genético que explora um conjunto de possibilidades para a determinação das ligas e utiliza uma heurística baseada em relaxação lagrangiana para determinação dos itens a serem produzidos. Além disso, uma abordagem para o mesmo problema é proposta utilizando o problema da mochila para determinar os itens a serem produzidos. Bons resultados foram obtidos pelos métodos propostos / According to a recent research made by the foundry sector, one of the most concern of the industry is to improve its production planning. A foundry production plan involves two independent stages: the determination of alloys to be merged and the lots that will be produced. In this work, we studied the lot-sizing problem for small foundries, whose purpose is to determine a plan of minimum production cost. As suggested in the literature, the heuristic proposed addresses the problem stages in a hierarchical way: rst we dene the alloys and, subsequently, the lots that are produced from them. We propose a genetic algorithm that explores some possible sets of alloys produced and uses a Lagrangian heuristic to determine the items to be produced. Also, we propose one approach to the same problem that uses the knapsack problem to determine the items to be produced. Good results were obtained by the methods proposed

O Homem tem desde o passado, tentado controlar a natureza. Um dos meios utilizados para isto, é sua observação do mundo. Através desta observação, tenta entender os fenômenos da natureza para fazer teorias e modelos. Charles Darwin, em seu trabalho Teoria da Evolução das Espécies, nos dá informações para o conhecimento de uma das mais importantes leis da natureza : sobrevive para a próxima geração o individuo mais forte. O Algoritmo Genético, pesquisado neste trabalho, é o exemplo disso. John Holland fez um Algoritmo Genético baseado na teoria de Darwin, que procura pelas melhores soluções para resolver um problema específico. Nada mais do que a simulação da teoria de Darwin. Nós pretendemos neste trabalho, estudar o Algoritmo Genético de Holland e através dele, analisar uma estrutura para encontrar seus parâmetros elásticos. / The men has since the past, tryed to control the nature. One of the way utilized for this, is his observation of the world. Through his observation, tries to understand the nature\'s fenomena, to making theories and models. Charles Darwin, in his work Theories of Species Evolution, gives us informations for the knowledges of one of the most important nature\'s laws: survives to the next generation the strongest individual . The Genetic Algorithm, the search in this work, is the example of this. John Holland, did a Genetic Algorithm. based in Darwin\'s Theories, that looks for the best solutions to solve a specific problem. Nothing else, of the simulation of the Darwin\'s theories . We intend in this work, to study the Holland\'s Genetic Algorithms and through it, to analyses a structure for find its elastic parameters.

The objective of this Master's thesis is to find, using genetic algorithm (GA), an optimal profile for lubricating gap of a thrust bearing of a turbocharger. Compared to the analytical profile, the optimal profile is expected to have minimized friction for an equivalent load capacity. Friction minimization is one way to increase the efficiency of the thrust bearing; it reduces the friction losses in the bearing. An initial problem was given: a thrust bearing with Load capacity 1000 N, inner and outer radii of 30mm and 60mm respectively, rotor speed of 45000 rpm and angle of running surface of $0.5^0$. Lubricant properties were also provided for the initial problem: oil density of $ 840 kg/m^3$, dynamic viscosity $(\eta)$ of 0.01 Pa.s With this data, the numerical solution of the Reynolds equation was computed using MATLAB. To obtain more information, the minimum lubricating gap thickness was also computed using MATLAB. With this information, the shape of the analytical profile, and its characteristics were found. The analytical profile was then used a guide to create a general profile. The general profile thus obtained is then optimized using GA. The characteristics of the generated profile is then computed and compared to that of the analytical profile.

The aim of this master thesis is optimization of the rotor in the terms of efficiency. An analysis of the effects of rotor parameters is performed by RMXPRT. A genetic algorithm is created to find the optimal solution. Solutions are evaluated and adjusted according to the results. Measurements on the actual machine are performed and evaluated to verify the functionality of the simulation models.

The problem of timetabling events is present in various organizations such as schools, hospitals, transportation centers. The purpose of timetabling activities at a university is to ensure that all students attend their required subjects in accordance with the available resources. The set of constraints that must be considered in the design of timetables involves students, teachers and infrastructure. This study shows that acceptable solutions are generated through the application of genetic, memetic and immune system algorithms for the problem of timetabling. The algorithms are applied to real instances of the University of Mumbai in India and their results are comparable with those of a human expert. / Revisión por pares