Browsing M.Sc. Computer Science by Title
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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.

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.

A RelationAlgebraic Approach to L  Fuzzy TopologyAny science deals with the study of certain models of the real world. However, a model is always an abstraction resulting in some uncertainty, which must be considered. The theory of fuzzy sets is one way of formalizing one of the types of uncertainty that occurs when modeling real objects. Fuzzy sets have been applied in various realworld problems such as control system engineering, image processing, and weather forecasting systems. This research focuses on applying the categorical framework of abstract L  fuzzy relations to Lfuzzy topology with ideas, concepts and methods of the theory of Lfuzzy sets. Since Lfuzzy sets were introduced to deal with the problem of approximate reasoning, t − norm based operations are essential in the definition of L  fuzzy topologies. We use the abstract theory of arrow categories with additional t − norm based connectives to define L  fuzzy topologies abstractly. In particular, this thesis will provide an abstract relational definition of an L  fuzzy topology, consider bases of topological spaces, continuous maps, and the first two separation axioms T0 and T1. The resulting theory of L  fuzzy topological spaces provides the foundation for applications and algorithms in areas such as digital topology, i.e., analyzing images using topological features.

RelMDDA Library for Manipulating Relations Based on MDDsRelation algebras is one of the stateoftheart means used by mathematicians and computer scientists for solving very complex problems. As a result, a computer algebra system for relation algebras called RelView has been developed at Kiel University. RelView works within the standard model of relation algebras. On the other hand, relation algebras do have other models which may have different properties. For example, in the standard model we always have L;L=L (the composition of two (heterogeneous) universal relations yields a universal relation). This is not true in some nonstandard models. Therefore, any example in RelView will always satisfy this property even though it is not true in general. On the other hand, it has been shown that every relation algebra with relational sums and subobjects can be seen as matrix algebra similar to the correspondence of binary relations between sets and Boolean matrices. The aim of my research is to develop a new system that works with both standard and nonstandard models for arbitrary relations using multiplevalued decision diagrams (MDDs). This system will implement relations as matrix algebras. The proposed structure is a library written in C which can be imported by other languages such as Java or Haskell.

The Salmon Algorithm  A New Population Based Search MetaheuristicThis thesis introduces the Salmon Algorithm, a search metaheuristic which can be used for a variety of combinatorial optimization problems. This algorithm is loosely based on the path finding behaviour of salmon swimming upstream to spawn. There are a number of tunable parameters in the algorithm, so experiments were conducted to find the optimum parameter settings for different search spaces. The algorithm was tested on one instance of the Traveling Salesman Problem and found to have superior performance to an Ant Colony Algorithm and a Genetic Algorithm. It was then tested on three coding theory problems  optimal edit codes, optimal Hamming distance codes, and optimal covering codes. The algorithm produced improvements on the best known values for five of six of the test cases using edit codes. It matched the best known results on four out of seven of the Hamming codes as well as three out of three of the covering codes. The results suggest the Salmon Algorithm is competitive with established guided random search techniques, and may be superior in some search spaces.

A Scalability Study and New Algorithms for LargeScale ManyObjective OptimizationMany realworld optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multiobjective problems (MOPs). Over the past few decades, a plethora of multiobjective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as manyobjective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a wellspread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for largescale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of manyobjective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming stateoftheart manyobjective algorithms.

Shortest Path Routing on the Hypercube with Faulty NodesInterconnection networks are widely used in parallel computers. There are many topologies for interconnection networks and the hypercube is one of the most popular networks. There are a variety of different routing paradigms that need to be investigated on the hypercube. In this thesis we investigate the shortest path routing between two nodes on the hypercube when some nodes are faulty and cannot be used. In this thesis the shortest path between two nodes is considered as the Hamming distance of them. Regarding the shortest path problem in a faulty hypercube, some efficient algorithms have been proposed when each processor (node) has limited information regarding the status of other processors (whether they are faulty or not). There are also some proposed algorithms for the case where there is no limitation on the data of each processor but they are not efficient and are exponential in terms of number of faulty nodes and dimension of the hypercube. To check whether there is a shortest path between two given nodes in a faulty hypercube, we propose a polynomial algorithm with time complexity of O(n^2 * m^2) where n is the dimension of the hypercube and m is the number of faulty nodes. Our algorithm only requires the source node to know the state of all other nodes. The proposed algorithm first checks whether there is a shortest path from the source node to the target node and then it can construct it efficiently. Our idea is based on a socalled ordering and permutation model of paths in the hypercube. We use a constructive approach to find the path which is a permutation as well. We then use inclusionexclusion and dynamic programming techniques to make our method efficient. We also propose an algorithm for counting all possible shortest paths in the hypercube.

Statistical Image Analysis for Image EvolutionThis thesis is focused on using genetic programming to evolve images based on lightweight features extracted from a given target image. The main motivation of this thesis is research by Lombardi et al. in which an image retrieval system is developed based on lightweight statistical features of images for comparing and classifying them in painting style categories; primarily based on color matching. In this thesis, automatic fitness scoring of variations of up to 17 lightweight image features using manyobjective fitness evaluation was used to evolve textures. Evolved results were shown to have similar color characteristics to target images. Although a human survey was conducted to confirm those results, it was inconclusive.

A Study of Ordered Gene Problems Featuring DNA Error Correction and DNA Fragment Assembly with a Variety of Heuristics, Genetic Algorithm Variations, and Dynamic RepresentationsOrdered gene problems are a very common classification of optimization problems. Because of their popularity countless algorithms have been developed in an attempt to find high quality solutions to the problems. It is also common to see many different types of problems reduced to ordered gene style problems as there are many popular heuristics and metaheuristics for them due to their popularity. Multiple ordered gene problems are studied, namely, the travelling salesman problem, bin packing problem, and graph colouring problem. In addition, two bioinformatics problems not traditionally seen as ordered gene problems are studied: DNA error correction and DNA fragment assembly. These problems are studied with multiple variations and combinations of heuristics and metaheuristics with two distinct types or representations. The majority of the algorithms are built around the Recentering Restarting Genetic Algorithm. The algorithm variations were successful on all problems studied, and particularly for the two bioinformatics problems. For DNA Error Correction multiple cases were found with 100% of the codes being corrected. The algorithm variations were also able to beat all other stateoftheart DNA Fragment Assemblers on 13 out of 16 benchmark problem instances.