Computational Intelligence has found a challenging testbed for various paradigms in the financial sector. Extensive research has resulted in numerous financial applications using neural networks and evolutionary computation, mainly genetic algorithms and genetic programming. More recent advances in the field of computational intelligence have not yet been applied as extensively or have not become available in the public domain, due to the confidentiality requirements of financial institutions. This study investigates how co-evolution together with the combination of par- ticle swarm optimisation and neural networks could be used to discover competitive security trading agents that could enable the timing of buying and selling securities to maximise net profit and minimise risk over time. The investigated model attempts to identify security trend reversals with the help of technical analysis methodologies. Technical market indicators provide the necessary market data to the agents and reflect information such as supply, demand, momentum, volatility, trend, sentiment and retracement. All this is derived from the security price alone, which is one of the strengths of technical analysis and the reason for its use in this study. The model proposed in this thesis evolves trading strategies within a single pop- ulation of competing agents, where each agent is represented by a neural network. The population is governed by a competitive co-evolutionary particle swarm optimi- sation algorithm, with the objective of optimising the weights of the neural networks. A standard feed forward neural network architecture is used, which functions as a market trend reversal confidence. Ultimately, the neural network becomes an amal- gamation of the technical market indicators used as inputs, and hence is capable of detecting trend reversals. Timely trading actions are derived from the confidence output, by buying and short selling securities when the price is expected to rise or fall respectively. No expert trading knowledge is presented to the model, only the technical market indicator data. The co-evolutionary particle swarm optimisation model facilitates the discovery of favourable technical market indicator interpretations, starting with zero knowledge. A competitive fitness function is defined that allows the evaluation of each solution relative to other solutions, based on predefined performance metric objectives. The relative fitness function in this study considers net profit and the Sharpe ratio as a risk measure. For the purposes of this study, the stock prices of eight large market capitalisation companies were chosen. Two benchmarks were used to evaluate the discovered trading agents, consisting of a Bollinger Bands/Relative Strength Index rule-based strategy and the popular buy-and-hold strategy. The agents that were discovered from the proposed hybrid computational intelligence model outperformed both benchmarks by producing higher returns for in-sample and out-sample data at a low risk. This indicates that the introduced model is effective in finding favourable strategies, based on observed historical security price data. Transaction costs were considered in the evaluation of the computational intelligent agents, making this a feasible model for a real-world application. Copyright / Dissertation (MSc)--University of Pretoria, 2010. / Computer Science / unrestricted

While it is important for systems intended to enhance musical creativity to define and explore musical ideas conceived by individual users, many limit musical freedom by focusing on maintaining musical structure, thereby impeding the user's freedom to explore his or her individual style. This dissertation presents a comprehensive body of work that introduces a new musical representation that allows users to explore a space of musical rules that are created from their own melodies. This representation, called functional scaffolding for musical composition (FSMC), exploits a simple yet powerful property of multipart compositions: The pattern of notes and rhythms in different instrumental parts of the same song are functionally related. That is, in principle, one part can be expressed as a function of another. Music in FSMC is represented accordingly as a functional relationship between an existing human composition, or scaffold, and an additional generated voice. This relationship is encoded by a type of artificial neural network called a compositional pattern producing network (CPPN). A human user without any musical expertise can then explore how these additional generated voices should relate to the scaffold through an interactive evolutionary process akin to animal breeding. The utility of this insight is validated by two implementations of FSMC called NEAT Drummer and MaestroGenesis, that respectively help users tailor drum patterns and complete multipart arrangements from as little as a single original monophonic track. The five major contributions of this work address the overarching hypothesis in this dissertation that functional relationships alone, rather than specialized music theory, are sufficient for generating plausible additional voices. First, to validate FSMC and determine whether plausible generated voices result from the human-composed scaffold or intrinsic properties of the CPPN, drum patterns are created with NEAT Drummer to accompany several different polyphonic pieces. Extending the FSMC approach to generate pitched voices, the second contribution reinforces the importance of functional transformations through quality assessments that indicate that some partially FSMC-generated pieces are indistinguishable from those that are fully human. While the third contribution focuses on constructing and exploring a space of plausible voices with MaestroGenesis, the fourth presents results from a two-year study where students discuss their creative experience with the program. Finally, the fifth contribution is a plugin for MaestroGenesis called MaestroGenesis Voice (MG-V) that provides users a more natural way to incorporate MaestroGenesis in their creative endeavors by allowing scaffold creation through the human voice. Together, the chapters in this dissertation constitute a comprehensive approach to assisted music generation, enabling creativity without the need for musical expertise.

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Approved for public release, distribution is unlimited / The United States Navy requires radical and innovative ways to model and design multifunction phased array radars. This thesis puts forth the concept that Genetic Algorithms, computer simulations that mirror the natural selection process to develop creative solutions to complex problems, would be extremely well suited in this application. The capability of a Genetic Algorithm to predict adequately the behavior of an array antenna with randomly located elements was verified with expected results through the design, construction, development and evaluation of a test-bed array. The test-bed array was constructed of commercially available components, including a unique and innovative application of a quadrature modulator microchip used in commercial communications applications. Corroboration of predicted beam patterns from both Genetic Algorithm and Method of Moments calculations was achieved in anechoic chamber measurements conducted with the test-bed array. Both H-plane and E-plane data runs were made with several phase steered beams. In all cases the measured data agreed with that predicted from both modeling programs. Although time limited experiments to beam forming and steering with phase shifting, the test-bed array is fully capable of beam forming and steering though both phase shifting and amplitude tapering. / Outstanding Thesis / Lieutenant Commander, United States Navy

[pt] Os Projetos de Eficiência Energética Gerenciados pelo Lado da Demanda (GLD), bem como todo projeto de engenharia, requerem decisões de investimentos que possuem incertezas associadas. As incertezas econômicas devem-se a fatores exógenos ao projeto sendo, em geral, representadas por oscilações estocásticas dos custos da energia elétrica. As incertezas técnicas estão associadas a fatores internos, como o desempenho dos projetos em função da tecnologia eficiente escolhida, da sua operação e manutenção. A decisão dos clientes e investidores na aquisição de Projetos de Eficiência Energética depende do retorno esperado nos ganhos com a energia economizada, como por exemplo, na venda desta energia no mercado de curto prazo. Esta tese investiga uma nova metodologia que, considerando as incertezas técnicas e econômicas, efetua uma análise mais abrangente e realista do cenário complexo de negócios que envolvem os Projetos de Eficiência Energética no Brasil. A metodologia contribui para a tomada de decisão considerando a flexibilidade gerencial e a avaliação dos riscos específicos dos projetos. Esta se baseia em técnicas inteligentes para a otimização de diagnósticos energéticos associados à análise de opções reais e avaliação econômica de Projetos de Eficiência Energética Gerenciados pelo Lado da Demanda (GLD), aplicados ao setor de energia elétrica no Brasil. A metodologia é avaliada em dois Projetos de Eficiência Energética, para os usos finais de Iluminação e Climatização de Ambientes, em uma unidade consumidora da classe Comercial, localizada na Cidade do Rio de Janeiro e conectada ao sistema de distribuição em Média Tensão (13,8kV). Os resultados revelaram que a partir da aplicação de Algoritmos Genéticos na otimização de diagnósticos energéticos puderam-se construir subprojetos originados de um projeto maior, mantendo-se, ou até ampliando-se a Relação Custo vs. Beneficio (RCB). E, desta forma, contribuir para a viabilização de alternativas ótimas de projetos que incentivam a aplicação da Eficiência Energética no Brasil. / [en] The Energy Efficiency Projects Managed by Demand Side (DSM), as well as all engineering design, require investment decisions that have associated uncertainties. Economic uncertainties are due to factors exogenous to the project being generally represented by stochastic fluctuations of electricity costs. The technical uncertainties are associated with internal factors such as performance of the projects on the basis of efficient technology chosen, its operation and maintenance. The decision of customers and investors in the acquisition of Energy Efficiency Projects depends on the expected return on the earnings of the energy saved, for example, the sale of this energy in the short term. This thesis investigates a new methodology which, considering the technical and economic uncertainties, performs a more comprehensive and realistic business complex scenario involving the Energy Efficiency Projects in Brazil. The methodology helps decision making considering managerial flexibility and risk assessment of specific projects. This is based on intelligent techniques for optimizing energy diagnoses associated with real options analysis and economic evaluation of Energy Efficiency Projects Managed by Demand Side (DSM), applied to the electricity sector in Brazil. The methodology is evaluated in two Energy Efficiency Projects for the end uses of lighting and Air Conditioning, in a consumer unit of the Commercial category, located in the city of Rio de Janeiro and connected to the distribution system in Medium Voltage (13.8kV). The results showed that with the application of genetic algorithms in optimization of energy diagnoses subprojects originated from a larger project could be built, maintaining or even widening the Cost vs. Value. Benefit (RCB) ratio. And in this way, contribute to the viability of alternative optimal designs that encourage the implementation of Energy Efficiency in Brazil.

[pt] Esta dissertação investiga o uso da Programação Genética para otimização estrutural de aglomerados de carbono. O objetivo primordial do estudo de cálculos que descrevam as interações de um aglomerado é encontrar o arranjo de átomos que corresponde à menor energia, ou àqueles que possuem energias próximas, já que estes são os candidatos mais prováveis de serem formados. Recentemente, na área da Inteligência Computacional, estudos apresentaram um novo método de otimização, chamado de Otimização por Programação Genética (OPG), com resultados promissores, avaliados em diversos casos de referência. A partir destes resultados, esta pesquisa aplica, de forma inédita, a abordagem OPG em problemas de otimização estrutural de aglomerados. Para fins de comparação, foram realizadas otimizações independentes utilizando o modelo tradicional de Algoritmos Genéticos (AGs). Neste trabalho, foram realizados vários ensaios computacionais utilizando os métodos OPG e AG para otimizar a geometria, ou seja, encontrar a estrutura de menor energia, de aglomerados de carbono de 5 a 25 átomos. Para o cálculo da energia, foi utilizado o potencial de Morse. Os valores das energias encontrados e as geometrias de cada aglomerado foram comparados com casos já publicados na literatura. Os resultados mostraram que, para os aglomerados menores, os dois métodos foram capazes de encontrar os mínimos globais, mas com o aumento do número de átomos, o OPG apresenta resultados superiores ao AG. Quanto ao tempo de execução por avaliação, o AG se mostrou significativamente mais rápido do que o do OPG, devido à sua representação direta das posições dos átomos, de um aglomerado, em um cromossomo. Porém a superioridade dos resultados OPG em relação ao AG indicou que a melhoria na sua implementação poderá ser de grande utilidade na área de simulação de aglomerados atômicos ou moleculares. / [en] This dissertation investigates the use of Genetic Programming for the structural optimization of carbon clusters. The main objective concerning computations that describe the interactions of a cluster is to find the arrangements of atoms corresponding to the lowest energy, since these are the most likely candidates to be formed. It has been recently introduced in the area of Computational Intelligence a new optimization method, called Optimization by Genetic Programming (OGP), showing promising results for several benchmark cases. Based on these results, the present work aimed at the application of OGP for the geometry optimization of carbon clusters. For comparison purposes, independent optimizations using the standard genetic algorithm (GA) approach were carried out. Several optimization trials were performed using both GA and OGP in order to find the best geometries of carbon clusters with size ranging from 5 to 25 atoms. The energy was calculated using the Morse potential. Resulting energies and geometries were compared to previously published results. Both GA and OGP were able to find the global minimum for the smaller clusters. However, upon increasing the number of atoms, the OGP presented better results compared to the GA. Concerning the execution time for each evaluation, the GA is significantly faster than the OGP due to its direct representation of the positions of atoms of a cluster in a chromosome. However, the superiority of the OGP results compared to the GA results suggests that an effort towards the improvement of the implementation of OGP could lead to a very powerful optimization tool to be used by the scientific community.

O Dilema do Prisioneiro com n jogadores é um problema que ilustra a dificuldade na formação da cooperação em sociedades de indivíduos racionais. Diversos trabalhos foram feitos no sentido de compreender melhor os fatores que influenciam o surgimento e a evolução da cooperação nessas sociedades, sendo que muitos desses mostraram que a simulação deste tipo de problema carece de escalabilidade, o que impede a realização de experimentos que envolvam uma grande quantidade de agentes ou de parâmetros de teste. Este trabalho tem o intuito de aplicar conceitos de computação paralela para tratar este problema. Para tal, foi desenvolvido um sistema denominado PS2 E2 , evolução de um trabalho anterior, cuja utilização em alguns cenários possibilitou a verificação da influência de alguns parâmetros tais como o tamanho da população e a expressividade do modelo de representação de estratégias na utilidade global de um conjunto de agentes que jogam o Dilema do Prisioneiro com n jogadores. / The n-Player Prisoners Dilemma is a problem that illustrates the difficulty of cooperation formation in societies composed of rational individuals. Several studies were made to better understand the factors that influence the emergence and evolution of cooperation in these societies. Many of these showed that the simulation of this type of problem lacks scalability, which hinders the achievement of experiments involving a large number of agents or test parameters. This work intends to apply parallel computing concepts to treat this problem. To this end, it was developed a system called PS2 E2 , an evolution of a previous work, whose utilization in some scenarios allowed the verification of the influence of some parameters such as the population size and the expressiveness of the strategy representation model in the global utility of a society of agents that play the n-Player Prisoner Dilemma.

[pt] Esta Pesquisa tem por objetivo utilizar a Regressão Simbólica por Programação Genética para encontrar uma equação analítica para a fronteira de exercício ótima (ou curva de gatilho) de uma opção sobre um ativo do qual o preço tem um comportamento simulado pelo processo estocástico conhecido como processo de reversão à média (PRM). Para o cálculo do valor de uma opção desde de sua aquisição até sua maturação, normalmente faz-se o uso do cálculo da fronteira de exercício ótimo. Esta curva separa ao longo do tempo a decisão de exercer ou não a opção. Sabendo-se que já existem soluções analíticas para calcular a fronteira de exercício ótimo quando o preço do ativo segue um Movimento Geométrico Browniano, e que tal solução genérica ainda não foi encontrada para o PRM, neste trabalho, foi proposto o uso da Programação Genética (PG) para encontrar tal solução analítica. A Programação Genética utilizou um conjunto de amostras de curvas de exercício ótimo parametrizadas segundo a variação da volatilidade e da taxa de juros livre de risco, para encontrar uma função analítica para a fronteira de exercício ótima, obtendo-se resultados satisfatórios. / [en] This research intends on to use the Symbolic Regression by Genetic Programming to find an analytical equation that represents an Optimal Exercise Boundary for an option of an asset having its price behavior simulated by a stochastic process known as Mean Reversion Process (MRP). To calculate an option value since its acquisition until its maturity, normally is used to calculate the Optimal Exercise Boundary. This frontier separates along the time the decision to exercise the option or not. Knowing there already are analytical solutions used to calculate the Optimal Exercise Boundary when the asset price follows the Geometric Brownian Motion, and such general solution was not found yet to MRP, in this work, it was proposed the use of Genetic Programming to find such analytical solution. The Genetic Programming used an amount of samples from optimal exercise curves parameterized according the change in the volatility and risk free interest rate, to find an analytical function that represents Optimal Exercise Boundary, achieving satisfactory results.