Efficient Training Set Use For Blood Pressure Prediction in a Large Scale Learning Classifier System

ABSTRACT We define a machine learning problem to forecast arterial blood pressure. Our goal is to solve this problem with a large scale learning classifier system. Because learning classifiers systems are extremely computationally intensive and this problem's eventually large training set will be very costly to execute, we address how to use less of the training set while not negatively impacting learning accuracy. Our approach is to allow competition among solutions which have not been evaluated on the entire training set. The best of these solutions are then evaluated on more of the training set while their offspring start off being evaluated on less of the training set. To keep selection fair, we divide competing solutions according to how many training examples they have been tested on

Dynamically adjusting game-play in 2D platformers using procedural level generation

The rapid growth of the entertainment industry has presented the requirement for more efficient development of computerized games. Importantly, the diversity of audiences that participate in playing games has called for the development of new technologies that allow games to address users with differing levels of skills and preferences. This research presents a systematic study that explored the concept of dynamic difficulty using procedural level generation with interactive evolutionary computation. Additionally, the design, development and trial of computerized agents the play game levels in the place of a human player is detailed. The work presented in this thesis provides a solution to the rapid growth of the entertainment industry whilst providing a more effective means for developing computerized games

Using Age Layered Population Structure for the Multi-Depot Vehicle Routing Problem

This thesis studies the NP-hard multi-depot vehicle routing problem (MDVRP) which is an extension of the classical VRP with the exception that vehicles based at one of several depots should service every customer assigned to that depot. Finding the optimal solution to MDVRP is computationally intractable for practical sized problem sets, and various meta-heuristics including genetic algorithms have been proposed in the literature. In this work, an e fficient multi-population genetic algorithm based on age layered population structures for the MDVRP is proposed. Three inter-layer transfer strategies are proposed and multi-objective fi tness evaluation is compared with weighted sum approach. An empirical study comparing the proposed approach with existing genetic algorithms and other meta-heuristics is carried out using well known benchmark data. The performance found in terms of solution quality is very promising

Energy service companies in China: The role of social networks and trust

China's energy-service companies (ESCOs) have developed only modestly despite favorable political and market conditions. We argue that with sophisticated market institutions still evolving in China, trust-based relations between ESCOs and energy customers are essential for successful implementation of energy efficiency projects. Chinese ESCOs, who are predominantly small and private enterprises, perform poorly in terms of trust-building because they are disembedded from local business, social, and political networks. We conclude that in the current institutional setting, the ESCO model based on market relations has serious limitations and is unlikely to lead to large-scale implementation of energy efficiency projects in China. --energy policies,energy service companies (ESCO),social networks,trust,China

The Watchmaker's guide to Artificial Life: On the Role of Death, Modularity and Physicality in Evolutionary Robotics

Photograph used for a newspaper owned by the Oklahoma Publishing Company

Simulations and Modelling for Biological Invasions

Biological invasions are characterized by the movement of organisms from their native geographic region to new, distinct regions in which they may have significant impacts. Biological invasions pose one of the most serious threats to global biodiversity, and hence significant resources are invested in predicting, preventing, and managing them. Biological systems and processes are typically large, complex, and inherently difficult to study naturally because of their immense scale and complexity. Hence, computational modelling and simulation approaches can be taken to study them. In this dissertation, I applied computer simulations to address two important problems in invasion biology. First, in invasion biology, the impact of genetic diversity of introduced populations on their establishment success is unknown. We took an individual-based modelling approach to explore this, leveraging an ecosystem simulation called EcoSim to simulate biological invasions. We conducted reciprocal transplants of prey individuals across two simulated environments, over a gradient of genetic diversity. Our simulation results demonstrated that a harsh environment with low and spatially-varying resource abundance mediated a relationship between genetic diversity and short-term establishment success of introduced populations rather than the degree of difference between native and introduced ranges. We also found that reducing Allee effects by maintaining compactness, a measure of spatial density, was key to the establishment success of prey individuals in EcoSim, which were sexually reproducing. Further, we found evidence of a more complex relationship between genetic diversity and long-term establishment success, assuming multiple introductions were occurring. Low-diversity populations seemed to benefit more strongly from multiple introductions than high-diversity populations. Our results also corroborated the evolutionary imbalance hypothesis: the environment that yielded greater diversity produced better invaders and itself was less invasible. Finally, our study corroborated a mechanical explanation for the evolutionary imbalance hypothesis – the populations evolved in a more intense competitive environment produced better invaders. Secondly, an important advancement in invasion biology is the use of genetic barcoding or metabarcoding, in conjunction with next-generation sequencing, as a potential means of early detection of aquatic introduced species. Barcoding and metabarcoding invariably requires some amount of computational DNA sequence processing. Unfortunately, optimal processing parameters are not known in advance and the consequences of suboptimal parameter selection are poorly understood. We aimed to determine the optimal parameterization of a common sequence processing pipeline for both early detection of aquatic nonindigenous species and conducting species richness assessments. We then aimed to determine the performance of optimized pipelines in a simulated inoculation of sequences into community samples. We found that early detection requires relatively lenient processing parameters. Further, optimality depended on the research goal – what was optimal for early detection was suboptimal for estimating species richness and vice-versa. Finally, with optimal parameter selection, fewer than 11 target sequences were required in order to detect 90% of nonindigenous species

Networks and navigation in the knowledge economy: Studies on the structural conditions and consequences of path-dependent and relational action

In the wake of a relational turn, economic geographers have begun to scrutinize the relationships and interactions between people and organizations as a driving force behind economic processes at both global and local scales. Through a focus on contingent contextuality and path dependence, relational economic geography and network thinking have provided the necessary conceptual toolbox for untangling the structural effects and drivers of these relationships and their spatial embeddedness. However, despite the conceptual richness of the relational approach, empirical studies have often fallen short of capturing its core tenets: First, there is a prevalence to focus on places, infrastructures, and similarities as aggregate proxies for actors and their socio-economic relationships as the unit of geographical network analysis; While often convenient, this approach misses out on the capacity of networks to represent spatially embedded social contexts as enablers or constraints of economic action. Second, while path dependence is at the heart of evolutionary approaches towards economic geography, few studies actually trace how path-dependent and interrelated innovation shapes the long-term emergence of fields. Relational processes are especially salient when outcomes are opaque, decisions are interdependent, and when formal rules and roles are weak or absent. In this thesis, I ask how actors navigate such contexts and investigate the structural conditions and consequences of their navigation efforts. In my pursuit of this question, I draw on literatures from sociology, economics, and organization studies and build on novel methods of network analysis capable of empirically capturing contextuality and path dependence to investigate relational processes at three levels of economic activity: The thesis first looks towards a localized and informal trade platform to demonstrate how consumers rely on their former transactions to navigate exchange uncertainty and how such an exchange system can become liable to personal lock-in. It then moves on to show how the geographically and organizationally diversified search for innovation opportunities structures the transfer of knowledge across a globalized and partially informal corporate scouting community. Finally, the thesis shows how the linkage of distinct knowledge domains drives the long-term emergence of heterogeneous technological fields. In its endeavor to trace these processes, the thesis contributes a set of distinct relational research designs that demonstrate how advances in methods and data can be employed to empirically exploit the conceptual richness of relational economic geography

Rules of engagement : competitive coevolutionary dynamics in computational systems

Given that evolutionary biologists have considered coevolutionary interactions since the dawn of Darwinism, it is perhaps surprising that coevolution was largely overlooked during the formative years of evolutionary computing. It was not until the early 1990s that Hillis' seminal work thrust coevolution into the spotlight. Upon attempting to evolve fixed-length sorting networks, a problem with a long and competitive history, Hillis found that his standard evolutionary algorithm was producing sub-standard networks. In response, he decided to reciprocally evolve a population of testlists against the sorting network population; thus producing a coevolutionary system. The result was impressive; coevolution not only outperformed evolution, but the best network it discovered was only one comparison longer than the best-known solution. For the first time, a coevolutionary algorithm had been successfully applied to problem-solving. Pre-Hillis, the shortcomings of standard evolutionary algorithms had been understood for some time: whilst defining an adequate fitness function can be as challenging as the problem one is hoping to solve, once achieved, the accumulation of fitness-improving mutations can push a population towards local optima that are difficult to escape. Coevolution offers a solution. By allowing the fitness of each evolving individual to vary (through competition) with other reciprocally evolving individuals, coevolution removes the requirement of a fitness yardstick. In conjunction, the reciprocal adaptations of each individual begin to erode local optima as soon as they appear. However, coevolution is no panacea. As a problem-solving tool, coevolutionary algorithms suffer from some debilitating dynamics, each a result of the relative fitness assessment of individuals. In a single-, or multi-, population competitive system, coevolution may stabilize at a suboptimal equilibrium, or mediocre stable state; analogous to the traditional problem of local optima. Populations may become highly specialized in an unanticipated (and undesirable) manner; potentially resulting in brittle solutions that are fragile to perturbation. The system may cycle; producing dynamics similar to the children's game rock-paper-scissors. Disengagement may occur, whereby one population out-performs another to the extent that individuals cannot be discriminated on the basis of fitness alone; thus removing selection pressure and allowing populations to drift. Finally, coevolution's relative fitness assessment renders traditional visualization techniques (such as the graph of fitness over time) obsolete; thus exacerbating each of the above problems. This thesis attempts to better understand and address the problems of coevolution through the design and analysis of simple coevolutionary models. 'Reduced virulence' - a novel technique specifically designed to tackle disengagement - is developed. Empirical results demonstrate the ability of reduced virulence to combat disengagement both in simple and complex domains, whilst outperforming the only known competitors. Combining reduced virulence with diversity maintenance techniques is also shown to counteract mediocre stability and over-specialization. A critique of the CIAO plot - a visualization technique developed to detect coevolutionary cycling - highlights previously undocumented ambiguities; experimental evidence demonstrates the need for complementary visualizations. Extending the scope of visualization, a first exploration into coevolutionary steering is performed; a technique allowing the user to interact with a coevolutionary system during run-time. Using a simple model incorporating reduced virulence, the coevolutionary steering demonstration highlights the future potential of such tools for both research and education. The role of neutrality in coevolution is discussed in detail. Whilst much emphasis is placed upon neutral networks in the evolutionary computation literature, the nature of coevolutionary neutrality is generally overlooked. Preliminary ideas for modelling coevolutionary neutrality are presented. Finally, whilst this thesis is primarily aimed at a computing audience, strong reference to evolutionary biology is made throughout. Exemplifying potential crossover, the CIAO plot, a tool previously unused in biology, is applied to a simulation of E. Coli, with results con rming empirical observations of real bacteria.EThOS - Electronic Theses Online ServiceGBUnited Kingdo