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京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第22898号 / 農博第2441号 / 新制||農||1083(附属図書館) / 学位論文||R3||N5318(農学部図書室) / 京都大学大学院農学研究科応用生物科学専攻 / (主査)教授 松浦 健二, 教授 大門 高明, 教授 寺内 良平 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Emerging infectious diseases of wildlife have threatened host populations of diverse taxa in recent history, which is largely attributable to anthropogenic global change. In three data chapters, this dissertation examines the drivers of individual- to population-level variation in how host populations respond to novel and emerging pathogens. Each chapter explores these processes in bat populations of North America, predominantly the Northeast and Midwest regions of the United States, impacted by the emerging fungal pathogen that causes white-nose syndrome, Pseudogymnoascus destructans. In Chapter 2, I disentangle the effects of adaptive host traits and environmental influences in driving host population stabilization of the little brown bat (Myotis lucifugus), finding that host-pathogen coexistence in this system is the product of their complex interaction. In Chapter 3, I characterize the range-wide variation in white-nose syndrome impacts on a federally endangered and poorly studied species, the Indiana bat (Myotis sodalis), as well as environmental and demographic determinants of its declines over epidemic time. In Chapter 4, I explore the role of individual variation in roosting microclimate selection of little brown bats in driving their infection severity, yielding important insights into the pathophysiology and environmental dependence of white-nose syndrome. Ultimately, this dissertation characterizes complex drivers of variation in host responses to emerging and invading pathogens, yielding insights essential to the successful mitigation of their impacts. / Doctor of Philosophy / In the same way that Covid-19 swept through our global human population in the year 2020, novel infectious diseases have threatened wildlife populations, sometimes to the point of extinction. Often, however, the processes driving the impacts of novel infectious diseases in wildlife are unknown, despite being important information to protect susceptible populations. In this dissertation, I explore how North American bat populations have been impacted by a recently emerged disease, white-nose syndrome, and what processes cause variation in how individual bats and bat colonies have responded to the disease. In Chapter 2, I explore how the little brown bat (Myotis lucifugus) has evolved to co-exist with its new pathogen and how this coexistence is affected by environmental conditions like temperature and humidity. In Chapter 3, I characterize variation in how populations of the Indiana bat (Myotis sodalis) have responded to white-nose syndrome and how environmental and demographic conditions have affected declines since the disease first emerged. In Chapter 4, I explore how the temperatures used by little brown bats during hibernation affect the severity of their infection, giving us important information on how bats survive with white-nose syndrome and the role of temperature. Altogether, the research in this dissertation describes complex interactions between hosts, pathogens, and their environment in driving the patterns we observe after the emergence of novel infectious diseases.

After the historic chess match between Deep Blue and Garry Kasparov, many researchers considered the game of chess solved and moved on to the more complex game of soccer. Artificial intelligence research has shifted focus to creating artificial players capable of mimicking the task of playing soccer. A new training algorithm is presented in this thesis for training teams of players from zero knowledge, evaluated on a simplified version of the game of soccer. The new algorithm makes use of the charged particle swarm optimiser as a neural network trainer in a coevolutionary training environment. To counter the lack of domain information a new relative fitness measure based on the FIFA league-ranking system was developed. The function provides a granular relative performance measure for competitive training. Gameplay strategies that resulted from the trained players are evaluated. It was found that the algorithm successfully trains teams of agents to play in a cooperative manner. Techniques developed in this study may also be widely applied to various other artificial intelligence fields. / Dissertation (MSc)--University of Pretoria, 2013. / Computer Science / unrestricted

This thesis presents Genetic Programming methodologies to find successful and understandable technical trading rules for financial markets. The methods when applied to the S&P500 consistently beat the buy-and-hold strategy over a 12-year period, even when considering transaction costs. Some of the methods described discover rules that beat the S&P500 with 99% significance. The work describes the use of a complexity-penalizing factor to avoid overfitting and improve comprehensibility of the rules produced by GPs. The effect of this factor on the returns for this domain area is studied and the results indicated that it increased the predictive ability of the rules. A restricted set of operators and domain knowledge were used to improve comprehensibility. In particular, arithmetic operators were eliminated and a number of technical indicators in addition to the widely used moving averages, such as trend lines and local maxima and minima were added. A new evaluation function that tests for consistency of returns in addition to total returns is introduced. Different cooperative coevolutionary genetic programming strategies for improving returns are studied and the results analyzed. We find that paired collaborator coevolution has the best results.

Phylogenetic trees capture the relationships between species and can be investigated by morphological and/or molecular data. When focusing on macroevolution, one considers the large-scale history of life with evolutionary changes affecting a single species of the entire clade leading to the enormous diversity of species obtained today. One major problem of biology is the explanation of this biodiversity. Therefore, one may ask which kind of macroevolutionary processes have given rise to observable tree shapes or patterns of species distribution which refers to the appearance of branching orders and time periods. Thus, with an increasing number of known species in the context of phylogenetic studies, testing hypotheses about evolution by analyzing the tree shape of the resulting phylogenetic trees became matter of particular interest. The attention of using those reconstructed phylogenies for studying evolutionary processes increased during the last decades. Many paleontologists (Raup et al., 1973; Gould et al., 1977; Gilinsky and Good, 1989; Nee, 2004) tried to describe such patterns of macroevolution by using models for growing trees. Those models describe stochastic processes to generate phylogenetic trees. Yule (1925) was the first who introduced such a model, the Equal Rate Markov (ERM) model, in the context of biological branching based on a continuous-time, uneven branching process. In the last decades, further dynamical models were proposed (Yule, 1925; Aldous, 1996; Nee, 2006; Rosen, 1978; Ford, 2005; Hernández-García et al., 2010) to address the investigation of tree shapes and hence, capture the rules of macroevolutionary forces. A common model, is the Aldous\\\' Branching (AB) model, which is known for generating trees with a similar structure of \\\"real\\\" trees. To infer those macroevolutionary forces structures, estimated trees are analyzed and compared to simulated trees generated by models. There are a few drawbacks on recent models such as a missing biological motivation or the generated tree shape does not fit well to one observed in empirical trees. The central aim of this thesis is the development and study of new biologically motivated approaches which might help to better understand or even discover biological forces which lead to the huge diversity of organisms. The first approach, called age model, can be defined as a stochastic procedure which describes the growth of binary trees by an iterative stochastic attachment of leaves, similar to the ERM model. At difference with the latter, the branching rate at each clade is no longer constant, but decreasing in time, i.e., with the age. Thus, species involved in recent speciation events have a tendency to speciate again. The second introduced model, is a branching process which mimics the evolution of species driven by innovations. The process involves a separation of time scales. Rare innovation events trigger rapid cascades of diversification where a feature combines with previously existing features. The model is called innovation model. Three data sets of estimated phylogenetic trees are used to analyze and compare the produced tree shape of the new growth models. A tree shape statistic considering a variety of imbalance measurements is performed. Results show that simulated trees of both growth models fit well to the tree shape observed in real trees. In a further study, a likelihood analysis is performed in order to rank models with respect to their ability to explain observed tree shapes. Results show that the likelihoods of the age model and the AB model are clearly correlated under the trees in the databases when considering small and medium-sized trees with up to 19 leaves. For a data set, representing of phylogenetic trees of protein families, the age model outperforms the AB model. But for another data set, representing phylogenetic trees of species, the AB model performs slightly better. To support this observation a further analysis using larger trees is necessary. But an exact computation of likelihoods for large trees implies a huge computational effort. Therefore, an efficient method for likelihood estimation is proposed and compared to the estimation using a naive sampling strategy. Nevertheless, both models describe the tree generation process in a way which is easy to interpret biologically. Another interesting field of research in biology is the coevolution between species. This is the interaction of species across groups such that the evolution of a species from one group can be triggered by a species from another group. Most prominent examples are systems of host species and their associated parasites. One problem is the reconciliation of the common history of both groups of species and to predict the associations between ancestral hosts and their parasites. To solve this problem some algorithmic methods have been developed in recent years. But only a few host parasite systems have been analyzed in sufficient detail which makes an evaluation of these methods complex. Within the scope of coevolution, the proposed age model is applied to the generation of cophylogenies to evaluate such host parasite reconciliation methods. The presented age model as well as the innovation model produce tree shapes which are similar to obtained tree structures of estimated trees. Both models describe an evolutionary dynamics and might provide a further opportunity to infer macroevolutionary processes which lead to the biodiversity which can be obtained today. Furthermore with the application of the age model in the context of coevolution by generating a useful benchmark set of cophylogenies is a first step towards systematic studies on evaluating reconciliation methods.

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Simulated annealing is a useful heuristic for finding good solutions for difficult combinatorial optimization problems. In some engineering applications the quality of a solution is based upon how tolerant the solution is to changes in the environment. The concept of simulated annealing is based upon the metallurgical process of annealing where a material is tempered by heating and cooling. Genetic algorithms have been used to evolve solutions to complex problems by imitating the biological process of evolution using crossover and mutation to modify the candidate solutions. In coevolution a candidate solution is composed of multiple species each of which provides a portion of the candidate solution. Those individuals of a species that, in collaboration with the individuals from the other species, are evaluated as providing the most fit solution are the preferred individuals of a species. This work investigated whether robustness, defined as the ability of a solution to tolerate changes to the problem environment, could be improved by defining a neighborhood of fitness functions that are centered in the neighborhood of the nominal objective function. Simulated annealing was used to manage the subsequent narrowing of the neighborhood of fitness functions. Two robustness measures were developed that used samples from the neighborhood of objective functions; one employed the minimum fitness value, and the other employed the average fitness value. Coevolutionary genetic algorithms were used to generate candidate solutions employing the robustness measures. This study used three benchmark functions to evaluate the effects of the robustness measures. The results indicated that the robustness measures could produce solutions that were robust and, often, globally optimal for benchmark functions employed in the testing. Future work includes applying this framework to a broader class of optimization problems, investigating new neighborhood strategies, and devising new robustness measures.

This thesis investigates the use of Bayesian analysis upon an opponent's behaviour in order to determine the desired goals or strategy used by a given adversary. A terrain analysis approach utilising the A* algorithm is investigated, where a probability distribution between discrete behaviours of an opponent relative to a set of possible goals is generated. The Bayesian analysis of agent behaviour accurately determines the intended goal of an opponent agent, even when the opponent's actions are altered randomly. The environment of Poker is introduced and abstracted for ease of analysis. Bayes' theorem is used to generate an effective opponent model, categorizing behaviour according to its similarity with known styles of opponent. The accuracy of Bayes' rule yields a notable improvement in the performance of an agent once an opponent's style is understood. A hybrid of the Bayesian style predictor and a neuroevolutionary approach is shown to lead to effective dynamic play, in comparison to agents that do not use an opponent model. The use of recurrence in evolved networks is also shown to improve the performance and generalizability of an agent in a multiplayer environment. These strategies are then employed in the full-scale environment of Texas Hold'em, where a betting round-based approach proves useful in determining and counteracting an opponent's play. It is shown that the use of opponent models, with the adaptive benefits of neuroevolution aid the performance of an agent, even when the behaviour of an opponent does not necessarily fit within the strict definitions of opponent 'style'.