It has been argued that much of evolution takes place in the absence of fitness gradients. Such periods of evolution can be analysed by examining the mutational network formed by sequences of equal fitness, that is, the neutral network. It has been demonstrated that, in large populations under a high mutation rate, the population distribution over the neutral network and average mutational robustness are given by the principal eigenvector and eigen- value, respectively, of the network's adjacency matrix. However, little progress has been made towards understanding the manner in which the topology of the neutral network influences the resulting population distribution and robustness. In this work, we build on recent results from spectral graph theory and utilize numerical methods to enhance our understanding of how populations distribute themselves over neutral networks. We demonstrate that, in the presence of certain topological features, the population will undergo an exploration catastrophe and become confined to a small portion of the network. We further derive approximations, in terms of mutational biases, for the population distribution and average robustness in networks with a homogeneous structure. The applicability of these results is explored, first, by a detailed review of the literature in both evolutionary computing and biology concerning the structure of neutral networks. This is extended by studying the actual and predicted population distribution over the neutral networks of H1N1 and H3N2 influenza haemagglutinin during seasons between 2005 and 2016. It is shown that, in some instances, these populations experience an exploration catastrophe. These results provide insight into the behaviour of populations on neutral networks, demonstrating that neutrality does not necessarily lead to an exploration of genotype/phenotype space or an associated increase in population diversity. Moreover, they provide a plausible explanation for conflicting results concerning the relationship between robustness and evolvability.

No description available.

No description available.

Writing a good story requires immense patience, creativity and work from the author, and the practice of writing a story requires a good grasp of the readers' psychology to create suspense and thrills and to merge the readers' world with that of the story. In the digital writing space, authors can still adhere to these rules of thumb while being aware of the disappearance of certain constraints due to the added possibility of narrating in a nonlinear fashion. There are many overlapping approaches to interactive storytelling or authoring, but each of the approaches has its own strengths and weaknesses. The motivation for this research arises from the perceived need for a new hybrid approach that coalesces and extends existing approaches. Since each of the approaches empowers certain aspects of the storytelling and narration process, the result forces a new research direction which eliminates certain weaknesses exhibited by a single approach, due to the synergistic nature of the various approaches. We have developed: 1) a Hybrid Evolutionary-Fuzzy Time-based Interactive (HEFTI) storytellling engine that generates dynamic stories from a set of authored story constructs given by human authors; 2) a set of authoring tools that allow authors to generate the needed story constructs; and, 3) a storytelling environment for them to orchestrate a digital stage play with computer agents and scripts. We have conducted a usability study and system evaluation to evaluate the performance of the engine. Our experiments and usability study have shown that the authoring environment abstracted the complexity of authoring an interactive, dynamic story from the authors with the use of windows-based interfaces to help them visualize various aspects of a story. This reduces the amount of learning and knowledge required to start having the pleasure of authoring dynamic stories. The studies also revealed certain features and tools that may be reflected by authoring tools in the future to automate various aspects of the authoring process so that the authors may spend more time thinking rather than writing (or programming) their stories.

The ultimate goal in robotics is to create machines which are more independent and rely less on humans to guide them in their operation. There are many sub-systems which may be present in such a robot, one of which is path planning — the ability to determine a sequence of positions or configurations between an initial and goal position within a particular obstacle cluttered workspace. Many classical path planning techniques have been developed, but these tend to have drawbacks such as their computational requirements; the suitability of the plans they produce for a particular application; or how well they are able to generalise to unseen problems. In recent years, evolutionary based problem solving techniques have seen a rise in popularity, possibly coinciding with the improvement in the computational power afforded researches by successful developments in hardware. These techniques adopt some of the features of natural evolution and mimic them in a computer. The increase in the number of publications in the areas of Genetic Algorithms (GA) and Genetic Programming (GP) demonstrate the success achieved when applying these techniques to ever more problem areas. This dissertation presents research conducted to determine whether there is a place for Evolutionary Approaches, and specifically GA and GP, in the development of future path planning techniques.

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.

No description available.

Le pilotage en temps réel des systèmes de production nécessite de prendre des décisions complexes sur l'affectation des ressources ou le choix des tâches à réaliser. Compte tenu de l'importance de la pertinence des décisions pour la performance d'un atelier, le pilotage fait l'objet de travaux de recherche dont l'objectif est d'aider les preneurs de décision. En particulier, on ne sait pas évaluer les conséquences sur la performance d'une décision en temps réel car les bonnes performances résultent d'une séquence de décisions et non d'une seule. De ce fait, il est difficile d'établir quelle est la meilleure décision à prendre à un instant donné. Plusieurs auteurs ont utilisé la simulation pour apprendre des bonnes pratiques à l'aide d'approches d'apprentissage automatique, mais se sont heurtés à la difficulté d'obtenir des exemples ou des observations sur des décisions en temps réel, où la prise en compte des changements d'états est indispensable pour choisir des stratégies de production. Nous avons réussi à aborder ce problème en proposant une approche d'apprentissage à l'aide de réseaux de neurones, qui ne nécessite pas d'exemples, d'observations ni de connaissances d'experts préalables. Ce type d'apprentissage s'effectue par optimisation via simulation des paramètres du réseau de neurones par rapport à un objectif de performance du système. Il vise à extraire de façon autonome des connaissances sur la meilleure façon de décider d'un modèle de simulation. Nous montrons la faisablité et l'apport de notre approche sur deux exemples inspirés de la littérature

A maioria dos algoritmos de aprendizado de máquina simbólico utilizam regras de conhecimento if-then como linguagem de descrição para expressar o conhecimento aprendido. O objetivo desses algoritmos é encontrar um conjunto de regras de classificação que possam ser utilizadas na predição da classe de novos casos que não foram vistos a priori pelo algoritmo. Contudo, este tipo de algoritmo considera o problema da interação entre as regras, o qual consiste na avaliação da qualidade do conjunto de regras induzidas (classificador) como um todo, ao invés de avaliar a qualidade de cada regra de forma independente. Assim, como os classificadores têm por objetivo uma boa precisão nos casos não vistos, eles tendem a negligenciar outras propriedades desejáveis das regras de conhecimento, como a habilidade de causar surpresa ou trazer conhecimento novo ao especialista do domínio. Neste trabalho, estamos interessados em construir regras de conhecimento com propriedades específicas de forma isolada, i.e. sem considerar o problema da interação entre as regras. Para esse fim, propomos uma abordagem evolutiva na qual cada individuo da população do algoritmo representa uma única regra e as propriedades específicas são codificadas como medidas de qualidade da regra, as quais podem ser escolhidas pelo especialista do domínio para construir regras com as propriedades desejadas. O algoritmo evolutivo proposto utiliza uma rica estrutura para representar os indivíduos (regras), a qual possibilita considerar uma grande variedade de operadores evolutivos. O algoritmo utiliza uma função de aptidão multi-objetivo baseada em ranking que considera de forma concomitante mais que uma medida de avaliação de regra, transformando-as numa função simples-objetivo. Como a avaliação experimental é fundamental neste tipo de trabalho, para avaliar nossa proposta foi implementada a Evolutionary Computing Learning Environment --- ECLE --- que é uma biblioteca de classes para executar e avaliar o algoritmo evolutivo sob diferentes cenários. Além disso, a ECLE foi implementada considerando futuras implementações de novos operadores evolutivos. A ECLE está integrada ao projeto DISCOVER, que é um projeto de pesquisa em desenvolvimento em nosso laboratório para a aquisição automática de conhecimento. Analises experimentais do algoritmo evolutivo para construir regras de conhecimento com propriedades específicas, o qual pode ser considerado uma forma de análise inteligente de dados, foram realizadas utilizando a ECLE. Os resultados mostram a adequabilidade da nossa proposta / Most symbolic machine learning approaches use if-then know-ledge rules as the description language in which the learned knowledge is expressed. The aim of these learners is to find a set of classification rules that can be used to predict new instances that have not been seen by the learner before. However, these sorts of learners take into account the rule interaction problem, which consists of evaluating the quality of the set of rules (classifier) as a whole, rather than evaluating the quality of each rule in an independent manner. Thus, as classifiers aim at good precision to classify unseen instances, they tend to neglect other desirable properties of knowledge rules, such as the ability to cause surprise or bring new knowledge to the domain specialist. In this work, we are interested in building knowledge rules with specific properties in an isolated manner, i.e. not considering the rule interaction problem. To this end, we propose an evolutionary approach where each individual of the algorithm population represents a single rule and the specific properties are encoded as rule quality measure, a set of which can be freely selected by the domain specialist. The proposed evolutionary algorithm uses a rich structure for individual representation which enables one to consider a great variety of evolutionary operators. The algorithm uses a ranking-based multi-objective fitness function that considers more than one rule evaluation measure concomitantly into a single objective. As experimentation plays an important role in this sort of work, in order to evaluate our proposal we have implemented the Evolutionary Computing Learning Environment --- ECLE --- which is a framework to evaluate the evolutionary algorithm in different scenarios. Furthermore, the ECLE has been implemented taking into account future development of new evolutionary operators. The ECLE is integrated into the DISCOVER project, a major research project under constant development in our laboratory for automatic knowledge acquisition and analysis. Experimental analysis of the evolutionary algorithm to construct knowledge rules with specific properties, which can also be considered an important form of intelligent data analysis, was carried out using ECLE. Results show the suitability of our proposal

No description available.