Browsing M.Sc. Computer Science by Title
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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.

ObjectOriented Genetic Programming for the Automatic Inference of Graph Models for Complex NetworksComplex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce wellknown graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Objective reduction in manyobjective optimization problemsManyobjective optimization problems (MaOPs) are multiobjective optimization problems which have more than three objectives. MaOPs face significant challenges because of search efficiency, computational cost, decision making, and visualization. Many wellknown multiobjective evolutionary algorithms do not scale well with an increasing number of objectives. The objective reduction can alleviate such difficulties. However, most research in objective reduction use nondominated sorting or Pareto ranking. However, Pareto is effective in problems having less than four objectives. In this research, we use two approaches to objective reduction: randombased and linear coefficientbased. We use the sum of ranks instead of Pareto Ranking. When applied to manyobjective problems, the sum of ranks has outperformed many other optimization approaches. We also use the age layered population structure (ALPS). We use ALPS in our approach to remove premature convergence and improve results. The performance of the proposed methods has been studied extensively on the famous benchmark problem DTLZ. The original GA and ALPS outperform the objective reduction algorithms in many test cases of DTLZ. Among all reduction algorithms, a linear coefficient based reduction algorithm provides better performance for some problems in this test suite. Random based reduction is not an appropriate strategy for reducing objectives.

Optimal Quaternary Hermitian Linear Complementary Dual Codes for EntanglementAssisted Quantum Error CorrectionThe objective of this thesis is to find suboptimal and optimal parameters from classical codes and import them into entanglementassisted quantum codes. The thesis begins by introducing classical error correction, followed by a detailed introduction to quantum computing. Topics that are discussed in the introduction include qubits, quantum phenomena, such as superposition and entanglement, and quantum gates/circuits. The thesis then reviews the basics of quantum error correction and provides Shor's code to reinforce the reader's understanding. Subsequently, the formalism of stabilizer codes is thoroughly examined. We then explain the generalized concept of stabilizer codes which is entanglementassisted quantum codes. They do not require generators to satisfy the commutativity property. Rather, they utilize the usage of ebits to resolve the anticommutativity constraint. Next, the thesis explains quaternary field and then the Java program implemented to find the optimal parameters. Lastly, the thesis concludes with presenting the parameters of the new codes that were obtained throughout the research. We have found the suboptimal largest distance for quaternary hermitian linear complementary dual codes that can be imported as entanglementassisted quantum error correction for parameters [22, 9, 9 or 10]₄, [22, 12, 7 or 8]₄, [23, 8, 11 or 12]₄, [23, 10, 9 or 10]₄, [23, 13, 7 or 8]₄, [24, 10, 10 or 11]₄, [24, 11, 9 or 10]₄, [24, 14, 7 or 8]₄, [25, 12, 9 or 10]₄, [25, 13, 8 or 9]₄, as well as the optimal largest distance for [17, 11, 5]₄ and [17, 13, 3]₄.

Particle swarm optimization for twoconnected networks with bounded ringsThe TwoConnected Network with Bounded Ring (2CNBR) problem is a network design problem addressing the connection of servers to create a survivable network with limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a stochastic populationbased optimization technique modeled on the social behaviour of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem. As PSO is originally designed to handle a continuous solution space, modification of the algorithm was necessary in order to adapt it for such a highly constrained discrete combinatorial optimization problem. Presented are an indirect transcription scheme for applying PSO to such discrete optimization problems and an oscillating mechanism for averting stagnation.

Passive Solar Building Design Using Genetic ProgrammingPassive 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 stringrewriting 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 multifloor 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.

Properties and algorithms of the (n, k)arrangement graphsThe (n, k)arrangement interconnection topology was first introduced in 1992. The (n, k )arrangement graph is a class of generalized star graphs. Compared with the well known nstar, the (n, k )arrangement graph is more flexible in degree and diameter. However, there are few algorithms designed for the (n, k)arrangement graph up to present. In this thesis, we will focus on finding graph theoretical properties of the (n, k) arrangement graph and developing parallel algorithms that run on this network. The topological properties of the arrangement graph are first studied. They include the cyclic properties. We then study the problems of communication: broadcasting and routing. Embedding problems are also studied later on. These are very useful to develop efficient algorithms on this network. We then study the (n, k )arrangement network from the algorithmic point of view. Specifically, we will investigate both fundamental and application algorithms such as prefix sums computation, sorting, merging and basic geometry computation: finding convex hull on the (n, k )arrangement graph. A literature review of the stateoftheart in relation to the (n, k)arrangement network is also provided, as well as some open problems in this area.

Properties and algorithms of the (n, k)star graphsThe (n, k)star interconnection network was proposed in 1995 as an attractive alternative to the nstar topology in parallel computation. The (n, k )star has significant advantages over the nstar which itself was proposed as an attractive alternative to the popular hypercube. The major advantage of the (n, k )star network is its scalability, which makes it more flexible than the nstar as an interconnection network. In this thesis, we will focus on finding graph theoretical properties of the (n, k )star as well as developing parallel algorithms that run on this network. The basic topological properties of the (n, k )star are first studied. These are useful since they can be used to develop efficient algorithms on this network. We then study the (n, k )star network from algorithmic point of view. Specifically, we will investigate both fundamental and application algorithms for basic communication, prefix computation, and sorting, etc. A literature review of the stateoftheart in relation to the (n, k )star network as well as some open problems in this area are also provided.

Properties and Algorithms of the (n,k)Arrangement Graphs and Augmented CubesThe (n, k)arrangement graph was first introduced in 1992 as a generalization of the star graph topology. Choosing an arrangement topology is more efficient in comparison with a star graph as we can have a closer number of nodes to what is needed. Also it has other advantages such as a lower degree and a smaller diameter, depending on k. In this thesis we investigate the problem of finding k(n − k) disjoint paths from a source node to k(n−k) target nodes in an (n, k)arrangement interconnection network such that no path has length more than diameter+(n−k)+2, where diameter is the maximum length of shortest path between any two nodes in the graph. These disjoint paths are built by routing to all neighbors of the source node and fixing specific elements in each of the k positions of the node representation in an (n, k)arrangement graph. Moreover, a simple routing is presented for finding n disjoint paths between two nodes which are located in different subgraphs. The lengths are no more than d(t, s) + 4, for d(t, s) being the shortest path length between two nodes s and t. This routing algorithm needs O(n^2) time to find all n these paths. In addition to arrangement graphs, we also study augmented cubes, first introduced in 2002, a desirable variation of the hypercube. An augmented cube of dimension n has a higher degree and a lower diameter in comparison with the hypercube. We introduce an O(n^3) algorithm for finding disjoint shortest paths from a single source node to 2n − 1 different target nodes.

Properties and algorithms of the hyperstar graph and its related graphsThe hyperstar interconnection network was proposed in 2002 to overcome the drawbacks of the hypercube and its variations concerning the network cost, which is defined by the product of the degree and the diameter. Some properties of the graph such as connectivity, symmetry properties, embedding properties have been studied by other researchers, routing and broadcasting algorithms have also been designed. This thesis studies the hyperstar graph from both the topological and algorithmic point of view. For the topological properties, we try to establish relationships between hyperstar graphs with other known graphs. We also give a formal equation for the surface area of the graph. Another topological property we are interested in is the Hamiltonicity problem of this graph. For the algorithms, we design an allport broadcasting algorithm and a singleport neighbourhood broadcasting algorithm for the regular form of the hyperstar graphs. These algorithms are both optimal timewise. Furthermore, we prove that the folded hyperstar, a variation of the hyperstar, to be maixmally faulttolerant.

Properties and Algorithms of the KCube GraphsThe KCube interconnection topology was rst introduced in 2010. The KCube graph is a compound graph of a Kautz digraph and hypercubes. Compared with the at tractive Kautz digraph and well known hypercube graph, the KCube graph could accommodate as many nodes as possible for a given indegree (and outdegree) and the diameter of interconnection networks. However, there are few algorithms designed for the KCube graph. In this thesis, we will concentrate on nding graph theoretical properties of the KCube graph and designing parallel algorithms that run on this network. We will explore several topological properties, such as bipartiteness, Hamiltonianicity, and symmetry property. These properties for the KCube graph are very useful to develop efficient algorithms on this network. We will then study the KCube network from the algorithmic point of view, and will give an improved routing algorithm. In addition, we will present two optimal broadcasting algorithms. They are fundamental algorithms to many applications. A literature review of the state of the art network designs in relation to the KCube network as well as some open problems in this field will also be given.

Properties and Algorithms of the KCube Interconnection NetworksThe KCube interconnection network was first introduced in 2010 in order to exploit the good characteristics of two wellknown interconnection networks, the hypercube and the Kautz graph. KCube links up multiple processors in a communication network with high density for a fixed degree. Since the KCube network is newly proposed, much study is required to demonstrate its potential properties and algorithms that can be designed to solve parallel computation problems. In this thesis we introduce a new methodology to construct the KCube graph. Also, with regard to this new approach, we will prove its Hamiltonicity in the general KC(m; k). Moreover, we will find its connectivity followed by an optimal broadcasting scheme in which a source node containing a message is to communicate it with all other processors. In addition to KCube networks, we have studied a version of the routing problem in the traditional hypercube, investigating this problem: whether there exists a shortest path in a Qn between two nodes 0n and 1n, when the network is experiencing failed components. We first conditionally discuss this problem when there is a constraint on the number of faulty nodes, and subsequently introduce an algorithm to tackle the problem without restrictions on the number of nodes.

Quadtree representation and compression of spatial dataSpatial data representation and compression has become a focus issue in computer graphics and image processing applications. Quadtrees, as one of hierarchical data structures, basing on the principle of recursive decomposition of space, always offer a compact and efficient representation of an image. For a given image, the choice of quadtree root node plays an important role in its quadtree representation and final data compression. The goal of this thesis is to present a heuristic algorithm for finding a root node of a region quadtree, which is able to reduce the number of leaf nodes when compared with the standard quadtree decomposition. The empirical results indicate that, this proposed algorithm has quadtree representation and data compression improvement when in comparison with the traditional method.

RealTime Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TMGenetic 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 realtime 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 blockclassifiers and the pixelclassifiers, were studied. Results showed that the pixelclassifiers generally performed better. Using these results, a suitable language was selected for the realtime 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 onpar with realtime requirements.

ReAlM  a system to manipulate relationsGiven a heterogeneous relation algebra R, it is well known that the algebra of matrices with coefficient from R is relation algebra with relational sums that is not necessarily finite. When a relational product exists or the point axiom is given, we can represent the relation algebra by concrete binary relations between sets, which means the algebra may be seen as an algebra of Boolean matrices. However, it is not possible to represent every relation algebra. It is well known that the smallest relation algebra that is not representable has only 16 elements. Such an algebra can not be put in a Boolean matrix form.[15] In [15, 16] it was shown that every relation algebra R with relational sums and subobjects is equivalent to an algebra of matrices over a suitable basis. This basis is given by the integral objects of R, and is, compared to R, much smaller. Aim of my thesis is to develop a system called ReAlM  Relation Algebra Manipulator  that is capable of visualizing computations in arbitrary relation algebras using the matrix approach.

Region Connection Calculus: Composition Tables and Constraint Satisfaction ProblemsQualitative spatial reasoning (QSR) is an important field of AI that deals with qualitative aspects of spatial entities. Regions and their relationships are described in qualitative terms instead of numerical values. This approach models human based reasoning about such entities closer than other approaches. Any relationships between regions that we encounter in our daily life situations are normally formulated in natural language. For example, one can outline one's room plan to an expert by indicating which rooms should be connected to each other. Mereotopology as an area of QSR combines mereology, topology and algebraic methods. As mereotopology plays an important role in region based theories of space, our focus is on one of the most widely referenced formalisms for QSR, the region connection calculus (RCC). RCC is a first order theory based on a primitive connectedness relation, which is a binary symmetric relation satisfying some additional properties. By using this relation we can define a set of basic binary relations which have the property of being jointly exhaustive and pairwise disjoint (JEPD), which means that between any two spatial entities exactly one of the basic relations hold. Basic reasoning can now be done by using the composition operation on relations whose results are stored in a composition table. Relation algebras (RAs) have become a main entity for spatial reasoning in the area of QSR. These algebras are based on equational reasoning which can be used to derive further relations between regions in a certain situation. Any of those algebras describe the relation between regions up to a certain degree of detail. In this thesis we will use the method of splitting atoms in a RA in order to reproduce known algebras such as RCC15 and RCC25 systematically and to generate new algebras, and hence a more detailed description of regions, beyond RCC25.