Browsing M.Sc. Computer Science by Title
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LFuzzy Relations in CoqHeyting categories, a variant of Dedekind categories, and Arrow categories provide a convenient framework for expressing and reasoning about fuzzy relations and programs based on those methods. In this thesis we present an implementation of Heyting and arrow categories suitable for reasoning and program execution using Coq, an interactive theorem prover based on HigherOrder Logic (HOL) with dependent types. This implementation can be used to specify and develop correct software based on Lfuzzy relations such as fuzzy controllers. We give an overview of lattices, Lfuzzy relations, category theory and dependent type theory before describing our implementation. In addition, we provide examples of program executions based on our framework.

LFuzzy Structured Query LanguageLattice valued fuzziness is more general than crispness or fuzziness based on the unit interval. In this work, we present a query language for a lattice based fuzzy database. We define a Lattice Fuzzy Structured Query Language (LFSQL) taking its membership values from an arbitrary lattice L. LFSQL can handle, manage and represent crisp values, linear ordered membership degrees and also allows membership degrees from lattices with noncomparable values. This gives richer membership degrees, and hence makes LFSQL more flexible than FSQL or SQL. In order to handle vagueness or imprecise information, every entry into an Lfuzzy database is an Lfuzzy set instead of crisp values. All of this makes LFSQL an ideal query language to handle imprecise data where some factors are noncomparable. After defining the syntax of the language formally, we provide its semantics using Lfuzzy sets and relations. The semantics can be used in future work to investigate concepts such as functional dependencies. Last but not least, we present a parser for LFSQL implemented in Haskell.

Landscape Aware Algorithm ConfigurationThe issue of parameter selection cannot be ignored if optimal performance is to be obtained from an algorithm on a specific problem or if a collection of algorithms are going to be compared in a fair manner. Unfortunately, adequately addressing the issue of parameter selection is time consuming and computationally expensive. Searching for appropriate control parameters generally requires much more time than actually solving the problem at hand due to the need to perform many complete runs of the target algorithm. The number of runs required to obtain thorough and equal coverage of the parameter space grows exponentially with the number of parameters. As a result, costs associated with parameter selection become a limiting factor in the scale of problems that can be investigated. The primary goal of this work is to reduce the costs of parameter selection. In pursuit of this goal, this thesis examines the use of neural networks to intelligently select appropriate control parameter values based on the characteristics of the problem at hand. Two general purpose approaches are evaluated: one that predicts a single set of control parameters to use throughout a run of the target algorithm; and, another that dynamically adjusts algorithm control parameters at run time. These approaches are examined in detail using the Particle Swarm Optimization algorithm. A comparison with state of the art automated tools for control parameter selection indicates that the cost of parameter selection can be significantly reduced.

Learning Strategies for Evolved Cooperating MultiAgent Teams in Pursuit DomainThis study investigates how genetic programming (GP) can be effectively used in a multiagent system to allow agents to learn to communicate. Using the predatorprey scenario and a cooperative learning strategy, communication protocols are compared as multiple predator agents learn the meaning of commands in order to achieve their common goal of first finding, and then tracking prey. This work is divided into three parts. The first part uses a simple GP language in the Pursuit Domain Development Kit (PDP) to investigate several communication protocols, and compares the predators' ability to find and track prey when the prey moves both linearly and randomly. The second part, again in the PDP environment, enhances the GP language and fitness measure in search of a better solution for when the prey moves randomly. The third part uses the Ms. PacMan Development Toolkit to test how the enhanced GP language performs in a game environment. The outcome of each part of this study reveals emergent behaviours in different forms of message sending patterns. The results from Part 1 reveal a general synchronization behaviour emerging from simple message passing among agents. Additionally, the results show a learned behaviour in the best result which resembles the behaviour of guards and reinforcements found in popular stealth video games. The outcomes from Part 2 reveal an emergent message sending pattern such that one agent is designated as the "sending" agent and the remaining agents are designated as "receiving" agents. Evolved agents in the Ms. PacMan simulator show an emergent sending pattern in which there is one agent that sends messages when it is in view of the prey. In addition, it is shown that evolved agents in both Part 2 and Part 3 are able to learn a language. For example, "sending" agents are able to make decisions about when and what type of command to send and "receiving" agents are able to associate the intended meaning to commands.

Lossy Compression of Quality Values in NextGeneration Sequencing DataIn this work we address the compression of SAM files which is the standard output file for DNA assembly. We specifically study lossy compression techniques used for quality values reported in the SAM file and we analyse the impact of such lossy techniques in the CRAM format. We also study the impact of these lossy techniques in the SNP calling process. Our results show that lossy techniques allow a better compression ratio than the one obtained with the original quality values. We also show that SNP calling performance is not negatively affected. Moreover we confirmed that some of the lossy techniques can even boost the SNP calling performance.

Managing Diversity and Many Objectives in Evolutionary DesignThis thesis proposes a new approach to evolving a diversity of highquality solutions for problems having many objectives. Mouret and Clune's MAPElites algorithm has been proposed as a way to evolve an assortment of diverse solutions to a problem. We extend MAPElites in a number of ways. Firstly, we introduce a manyobjective strategy called sumofranks, which enables problems with many objectives (4 and more) to be considered in the MAP. Secondly, we enhance MAPElites by extending it with multiple solutions per "grid" cell (the original MAPElites saves only a single solution per cell). A few different ways of selecting cell members for reproduction are also considered. We test the new MAPElites strategies on the evolutionary art application of image generation. Using procedural textures, genetic programming is used with upwards of 15 lightweight image features to guide fitness. The goal is to evolve images that share image features with a given target image. Our experiments show that the new MAPElites algorithms produce a large number of diverse solutions of varying quality. The extended MAPElites algorithm is also statistically competitive compared to vanilla GP in this application domain.

MDPbased Vehicular Network Connectivity Model for VCC ManagementVehicular Cloud computing is a new paradigm in which vehicles collaboratively exchange data and resources to support services and problemsolving in urban environments. Characteristically, such Clouds undergo severe challenging conditions from the high mobility of vehicles, and by essence, they are rather dynamic and complex. Many works have explored the assembling and management of Vehicular Clouds with designs that heavily focus on mobility. However, a mobilitybased strategy relies on vehicles' geographical position, and its feasibility has been questioned in some recent works. Therefore, we present a more relaxed Vehicular Cloud management scheme that relies on connectivity. This work models uncertainty and considers every possible chance a vehicle may be available through accessible communication means, such as vehicletoeverything (V2X) communications and the vehicle being in the range of roadside units (RSUs) for data transmissions. We propose an markovdecisision process (MDP) model to track vehicles' connection status and estimate their reliability for data transmissions. Also, from analyses, we observed that higher vehicle connectivity presents a trace of repeated connection patterns. We reinforce the connectivity status by validating it through an availability model to distinguish the vehicles which support high availability regardless of their positioning. The availability model thus determines the suitability of the MDP model in a given environment.

Mixed Media in Evolutionary ArtThis 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.

Modal and Relevance Logics for Qualitative Spatial ReasoningQualitative Spatial Reasoning (QSR) is an alternative technique to represent spatial relations without using numbers. Regions and their relationships are used as qualitative terms. Mostly peer qualitative spatial reasonings has two aspect: (a) the first aspect is based on inclusion and it focuses on the ”partof” relationship. This aspect is mathematically covered by mereology. (b) the second aspect focuses on topological nature, i.e., whether they are in ”contact” without having a common part. Mereotopology is a mathematical theory that covers these two aspects. The theoretical aspect of this thesis is to use classical propositional logic with nonclassical relevance logic to obtain a logic capable of reasoning about Boolean algebras i.e., the mereological aspect of QSR. Then, we extended the logic further by adding modal logic operators in order to reason about topological contact i.e., the topological aspect of QSR. Thus, we name this logic Modal Relevance Logic (MRL). We have provided a natural deduction system for this logic by defining inference rules for the operators and constants used in our (MRL) logic and shown that our system is correct. Furthermore, we have used the functional programming language and interactive theorem prover Coq to implement the definitions and natural deduction rules in order to provide an interactive system for reasoning in the logic.

Modeling Metal Protein Complexes from Experimental Extended Xray Absorption Fine Structure using Computational IntelligenceExperimental Extended Xray Absorption Fine Structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, pre dicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proven somewhat successful in structure refinement but have not been successful in finding the global minima. Multiple population based algorithms, including a genetic algorithm, a restarting ge netic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure refinement of EXAFS. The oxygenevolving com plex in S1 is used as a benchmark for comparing the algorithms. These algorithms were successful in finding new atomic structures that produced improved calculated EXAFS spectra over atomic structures previously found.

Modelling and Proving Cryptographic Protocols in the Spi Calculus using CoqThe spi calculus is a process algebra used to model cryptographic protocols. A process calculus is a means of modelling a system of concurrently interacting agents, and provides tools for the description of communications and synchronizations between those agents. The spi calculus is based on Robin Milner's pi calculus, which was itself based upon his Calculus of Communicating Systems (CCS). It was created by Martin Abadi and Andrew D. Gordon as an expansion of the pi calculus intended to focus on cryptographic protocols, and adds features such as the encryption and decryption of messages using keys. The Coq proof system is an interactive theorem prover that allows for the definition of types and functions, and provides means by which to prove properties about them. The spi calculus has been implemented in Coq and subsequently used to model and show an example proof of a property about a simple cryptographic protocol. This required the implementation of both the syntax and the semantics of the calculus, as well as the rules and axioms used to manipulate the syntax in proofs. We discuss the spi calculus in detail as defined by Abadi and Gordon, then the various challenges faced during the implementation of the calculus and our rationale for the decisions made in the process.

MultiGuide Particle Swarm Optimization for LargeScale MultiObjective Optimization ProblemsMultiguide particle swarm optimization (MGPSO) is a novel metaheuristic for multiobjective optimization based on particle swarm optimization (PSO). MGPSO has been shown to be competitive when compared with other stateoftheart multiobjective optimization algorithms for lowdimensional problems. However, to the best of the author’s knowledge, the suitability of MGPSO for highdimensional multiobjective optimization problems has not been studied. One goal of this thesis is to provide a scalability study of MGPSO in order to evaluate its efficacy for highdimensional multiobjective optimization problems. It is observed that while MGPSO has comparable performance to stateoftheart multiobjective optimization algorithms, it experiences a performance drop with the increase in the problem dimensionality. Therefore, a main contribution of this work is a new scalable MGPSObased algorithm, termed cooperative coevolutionary multiguide particle swarm optimization (CCMGPSO), that incorporates ideas from cooperative PSOs. A detailed empirical study on wellknown benchmark problems comparing the proposed improved approach with various stateoftheart multiobjective optimization algorithms is done. Results show that the proposed CCMGPSO is highly competitive for highdimensional problems.

A MultiObjective Genetic Algorithm with Side Effect Machines for Motif DiscoveryUnderstanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multiobjective 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 multiobjective 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.

Multiobjective Genetic Algorithms for Multidepot VRP with Time WindowsEfficient routing and scheduling has significant economic implications for many realworld situations arising in transportation logistics, scheduling, and distribution systems, among others. This work considers both the single depot vehicle routing problem with time windows (VRPTW) and the multidepot vehicle routing problem with time windows (MDVRPTW). An agelayered population structure genetic algorithm is proposed for both variants of the vehicle routing problem. To the best of the author’s knowledge, this is first work to provide a multiobjective genetic algorithm approach for the MDVRPTW using wellknown benchmark data with up to 288 customers.

MultiObjective Genetic Algorithms for the Single Allocation Hub Location ProblemHub 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 origindestination 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 singleobjective criterion measure for the problem is ﬁrst explored. Next, a multiobjective GA employing various ﬁtness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multiobjective GA is shown by comparison with an Integer Programming strategy, the only other multiobjective 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.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic ProgrammingA 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 realworld 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 wellknown network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed metaanalysis 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 GPbased automatic inference system was used to reproduce existing, wellknown graph models as well as a realworld 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.

Neural Network Guided Evolution of Lsystem PlantsA Lindenmayer system is a parallel rewriting system that generates graphic shapes using several rules. Genetic programming (GP) is an evolutionary algorithm that evolves expressions. A convolutional neural network(CNN) is a type of neural network which is useful for image recognition and classification. The goal of this thesis will be to generate different styles of Lsystem based 2D images of trees from scratch using genetic programming. The system will use a convolutional neural network to evaluate the trees and produce a fitness value for genetic programming. Different architectures of CNN are explored. We analyze the performance of the system and show the capabilities of the combination of CNN and GP. We show that a variety of interesting tree images can be automatically evolved. We also found that the success of the system highly depends on CNN training, as well as the form of the GP's Lsystem language representation.

New Contig Creation Algorithm for the de novo DNA Assembly ProblemDNA assembly is among the most fundamental and difficult problems in bioinformatics. Near optimal assembly solutions are available for bacterial and small genomes, however assembling large and complex genomes especially the human genome using NextGenerationSequencing (NGS) technologies is shown to be very difficult because of the highly repetitive and complex nature of the human genome, short read lengths, uneven data coverage and tools that are not specifically built for human genomes. Moreover, many algorithms are not even scalable to human genome datasets containing hundreds of millions of short reads. The DNA assembly problem is usually divided into several subproblems including DNA data error detection and correction, contig creation, scaffolding and contigs orientation; each can be seen as a distinct research area. This thesis specifically focuses on creating contigs from the short reads and combining them with outputs from other tools in order to obtain better results. Three different assemblers including SOAPdenovo [Li09], Velvet [ZB08] and Meraculous [CHS+11] are selected for comparative purposes in this thesis. Obtained results show that this thesis’ work produces comparable results to other assemblers and combining our contigs to outputs from other tools, produces the best results outperforming all other investigated assemblers.

Nonphotorealistic Rendering with Cartesian Genetic Programming using Graphic Processing UnitsNonphotorealistic 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 treebased approaches. Implementation of the system with GPUs showed significant speedup.

Object Classification using LFuzzy Concept AnalysisObject classification and processing have become a coordinated piece of modern industrial manufacturing systems, generally utilized in a manual or computerized inspection process. Vagueness is a common issue related to object classification and analysis such as the ambiguity in input data, the overlapping boundaries among the classes or regions, and the indefiniteness in defining or extracting features and relations among them. The main purpose of this thesis is to construct, define, and implement an abstract algebraic framework for Lfuzzy relations to represent the uncertainties involved at every stage of the object classification. This is done to handle the proposed vagueness that is found in the process of object classification such as retaining information as much as possible from the original data for making decisions at the highest level making the ultimate output or result of the associated system with least uncertainty.