Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

A stochastic dynamic programming approach for pricing options on stock-index futures

The aim of this thesis is to price options on equity index futures with an application to standard options on S&P 500 futures traded on the Chicago Mercantile Exchange. Our methodology is based on stochastic dynamic programming, which can accommodate European as well as American options. The model accommodates dividends from the underlying asset. It also captures the optimal exercise strategy and the fair value of the option. This approach is an alternative to available numerical pricing methods such as binomial trees, finite differences, and ad-hoc numerical approximation techniques. Our numerical and empirical investigations demonstrate convergence, robustness, and efficiency. We use this methodology to value exchange-listed options. The European option premiums thus obtained are compared to Black's closed-form formula. They are accurate to four digits. The American option premiums also have a similar level of accuracy compared to premiums obtained using finite differences and binomial trees with a large number of time steps. The proposed model accounts for deterministic, seasonally varying dividend yield. In pricing futures options, we discover that what matters is the sum of the dividend yields over the life of the futures contract and not their distribution.

Genetic Programming for Non-Photorealistic Rendering

This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Reading Rocks Junior: A Mixed-Methodological Study of the Efficacy and Accessibility of an Emergent Literacy Intervention

This thesis explored early literacy development in young vulnerable readers. More specifically, this thesis examined an emergent literacy program called Reading Rocks Junior offered by the Learning Disabilities Association of Niagara Region to children four- to six-years of age living in low socioeconomic status communities. Three methodologies were combined to create a rich and complete picture of an effective and accessible literacy program. First of all, a description of the Reading Rocks Junior program is outlined. Secondly, quantitative data that was collected pre- and post- program was analyzed to demonstrate achievement gains made as a result of participating in the program. Finally, qualitative interviews with the program coordinator, the convener of the agency that funded Reading Rocks Junior and three parents whose children participated in the program were analyzed to determine the contextual factors that make Reading Rocks Junior a success.

Multi-Objective Genetic Algorithms for the Single Allocation Hub Location Problem

Hub Location Problems play vital economic roles in transportation and telecommunication networks where goods or people must be efficiently transferred from an origin to a destination point whilst direct origin-destination links are impractical. This work investigates the single allocation hub location problem, and proposes a genetic algorithm (GA) approach for it. The effectiveness of using a single-objective criterion measure for the problem is first explored. Next, a multi-objective GA employing various fitness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multi-objective GA is shown by comparison with an Integer Programming strategy, the only other multi-objective approach found in the literature for this problem. Lastly, two new crossover operators are proposed and an empirical study is done using small to large problem instances of the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets.

Passive Solar Building Design Using Genetic Programming

Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition, window and volume based objectives are tried. To examine the impact of environment on designs, experiments are run on five different geographic locations. Also, both single floor models and multi-floor models are examined in this research. According to the experiments, solutions from the experiments were consistent with respect to materials, sizes, and appearance, and satisfied problem constraints in all instances.

Can Mixed Martial Arts be Ethically Defended?: Autonomy, Paternalism and the Harm Principle

Abstract Mixed Martial Arts (MMA) and the Ultimate Fighting Championship (UFC) founded in 1993 have been under scrutiny for the past two decades. Unlike boxing, the ethical status of MMA and whether it is morally defensible have rarely been analyzed in the academic literature. I argue that MMA requires such an analysis because it is inherently violent. The purpose of this study was to examine elite-level MMA by referring to the ethical concepts of autonomy, paternalism and the Harm Principle. Findings from interviews with MMA athletes as well as my personal experience of MMA were presented to establish a deeper understanding of the sport and what it means to train and compete in a sport defined as violent. The conceptual analysis and findings of MMA athletes' experiences in this investigation resulted in the conclusion that MMA is ethically defensible. Additional findings, implications and recommendations for further research were also discussed.

Becoming Peer Health Leaders: A Mixed-Methods Evaluation of a Youth Leadership Camp Preparing Peer Leaders for Participation in a Health Promotion Initiative.

Youth are critical partners in health promotion, but the process of training young people to become meaningfully involved is challenging. This mixed-methods evaluation considered the impact of a leadership camp in preparing 42 grade seven students to become peer health leaders in a ‘heart health’ initiative. The experiences of participants and their sense of agency were explored. Data were collected from pre and post camp surveys, focus groups, student journals and researcher observations. Findings indicate that relationships with peers and adults were key to agency development, and participants appeared to broaden their perspectives on the meanings of ‘health’ and ‘leadership.’ Significant changes on two sub-scales of the Harter Perceived Competence Scale for Children were also found. Suggestions for practice and further research are provided.

Parents of the Gifted in Ontario: An Investigation of Parental Satisfaction with the Education of their Gifted Children

The opinions of parents in relation to the education of their gifted child were examined, with particular attention paid to their satisfaction and the type and amount of programming their child is receiving. This study employed a mixed methods research design that focused on parents’ experiences with gifted education programming and their perceptions and level of satisfaction with these programs. A survey was used to gather the perceptions and opinions of parents of gifted children in Ontario. The data were quantified and used to make observations in relation to differences in parental satisfaction and to provide a more thorough understanding of the experiences of parents in Ontario in regards to the education of gifted children. Information was also gathered regarding the recommendations that parents have for the improvement of education for their gifted child. The results of the study found that parents of gifted children were satisfied with the connections their child made within a gifted placement with like-minded peers and with opportunities for their children to learn in a more individualized and in-depth manner. However, parents expressed dissatisfaction with the timing of the initial gifted identification and the lack of knowledge that teachers, in both regular and specialized classrooms, have about gifted children and the types of programming best suited to these children. The results of the study also showed parental dissatisfaction with the lack of funding allocated to gifted education programs by district school boards and the lack of involvement they were allowed with respect to the education of their child.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Explorations of Intramolecular [5+2] Cycloadditions of Ring-Constrained Vinylcyclopropanes

The first example of a [5+2] cycloaddition reaction wherein the olefin of the vinylcyclopropyl moiety is constrained in a carbocycle was explored, and possible reasons on the lack of reactivity of the substrate were studied. A simple model substrate was synthesized and subjected to cycloaddition conditions to determine if the reason for the lack of reactivity was related to the complexity of the substrate, or if the lack of “conjugative character” of the cyclopropyl ring with respect to the olefin is responsible. A more complex bicyclic substrate possessing an angular methyl group at the ring junction was also synthesized and explored, with evidence supporting the current theory of deconjugation of the cyclopropyl moiety.

A Mixed-Method Study of Educator Knowledge and Practice Related to Student Socio-moral Development

The purpose of this study was to explore elementary educators’ knowledge of moral development, how this knowledge relates to their beliefs and sense of efficacy pertaining to character education practices and the socio-moral reasoning of their students. It was hypothesized that educators’ beliefs and practices related to character education would reflect their pedagogy rather than knowledge of moral development theory. It was further hypothesized that there would be differences in student socio-moral reasoning specifically the beliefs and desires that guide actions would differ based on grade and gender. This mixed-method study employing self-report questionnaires, open response vignettes, and semi-structured educator interviews yielded quantitative and qualitative data. Findings indicated socio-moral reasoning of students differed according to grade (age) and gender. Knowledge of moral development theory was found to vary among participants however some practices employed by educators did align with a social cognitive approach to moral development. Significant variables identified consistently among educator and student participants included, autonomy, social competence, sense of school community, and supportiveness. These variables, in conjunction with a sense of fairness, have been identified elsewhere as foundational to moral development (Nucci, 2009), and intrinsic motivation (Ryan & Deci, 2000) and are relevant to educators working to develop student socio-moral reasoning as an aspect of character.