• Epidemic Simulation and Mitigation via Evolutionary Computation

      Dubé, Michael; Department of Computer Science
      A global pandemic remains a public health event that presents a unique and unpredictable challenge for those making health related decisions and the populations who experience the virus. Though a pandemic also provides the opportunity for researchers and health administrations around the world to mobilize in the fields of epidemiology, computer science, and mathematics to generate epidemic models, vaccines, and vaccination strategies to mitigate unfavourable outcomes. To this end, a generative representation to create personal contact networks, representing the social connections within a population, known as the Local THADS-N generative representation is introduced and expanded upon. This representation uses an evolutionary algorithm and is modified to include new local edge operations improving the performance of the system across several test problems. These problems include an epidemic's duration, spread through a population, and closeness to past epidemic behaviour. The system is further developed to represent sub-communities known as districts, better articulating epidemics spreading within and between neighbourhoods. In addition, the representation is used to simulate four competing vaccination strategies in preparation for iterative vaccine deployment amongst a population, an inevitability when considering the lag inherent to developing vaccines. Finally, the Susceptible-Infected-Removed (SIR) model of infection used by the system is expanded in preparation for adding an asymptomatic state of infection as seen within the COVID-19 pandemic.
    • Inverse Illumination Design with Genetic Programming

      Moylan, Kelly; Department of Computer Science
      Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.
    • Mixed Media in Evolutionary Art

      Maslen, Jordan; Department of Computer Science
      This thesis focuses on creating evolutionary art with genetic programming. The main goal of the system is to produce novel stylized images using mixed media. Mixed media on a canvas is the use of multiple artistic effects being used to produce interesting and new images. This approach uses a genetic program (GP) in which each individual in the population will represent their own unique solution. The evaluation method being used to determine the fitness of each individual will be direct colour matching of the GP canvas and target image. The secondary goal was to see how well different computer graphic techniques work together. In particular, bitmaps have not been studied much in evolutionary art. Results show a variety of unique solutions with the application of mixed media.
    • A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

      Alizadeh Noori, Farhad; Department of Computer Science (Brock University, 2012-09-18)
      Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.
    • Non-photorealistic Rendering with Cartesian Genetic Programming using Graphic Processing Units

      Bakurov, Illya; Department of Computer Science
      Non-photorealistic rendering (NPR) is concerned with the algorithm generation of images having unrealistic characteristics, for example, oil paintings or watercolour. Using genetic programming to evolve aesthetically pleasing NPR images is a relatively new approach in the art field, and in the majority of cases it takes a lot of time to generate results. With use of Cartesian genetic programming (CGP) and graphic processing units (GPUs), we can improve the performance of NPR image evolution. Evolutionary NPR can render images with interesting, and often unexpected, graphic effects. CGP provides a means to eliminate large, inefficient rendering expressions, while GPU acceleration parallelizes the calculations, which minimizes the time needed to get results. By using these tools, we can speed up the image generation process. Experiments revealed that CGP expressions are more concise, and search is more exploratory, than in tree-based approaches. Implementation of the system with GPUs showed significant speed-up.