23 research outputs found
An Approach to Pattern Recognition by Evolutionary Computation
Evolutionary Computation has been inspired by the natural phenomena of evolution. It provides a quite general heuristic, exploiting few basic concepts: reproduction of individuals, variation phenomena that affect the likelihood of survival of individuals, inheritance of parents features by offspring. EC has been widely used in the last years to effectively solve hard, non linear and very complex problems.
Among the others, ECābased algorithms have also been used to tackle
classification problems. Classification is a process according to which an object is attributed to one of a finite set of classes or, in other words, it is recognized as belonging to a set of equal or similar entities, identified by a label. Most likely, the main aspect of classification concerns the generation of prototypes to be used to recognize unknown patterns. The role of prototypes is that of representing patterns belonging to the different classes defined within a given problem. For most of the problems of practical interest, the generation of such prototypes is a very hard problem, since a prototype must be able to represent patterns belonging to the same class, which may be significantly dissimilar each other. They must also be able to discriminate patterns belonging to classes different from the one that they represent. Moreover, a prototype should contain the minimum amount of information required to satisfy the requirements just mentioned. The research presented in this thesis, has led to the definition of an ECābased framework to be used for prototype generation. The defined framework does not provide for the use of any particular kind of prototypes. In fact, it can generate any kind of prototype once an encoding scheme for the used prototypes has been defined. The generality of the framework can be exploited to develop many applications. The framework has been employed to implement two specific applications for prototype generation.
The developed applications have been tested on several data sets and the results compared with those obtained by other approaches previously presented in the literature
Memory system for a relational database processor
An associative memory for a relational database management system, with content addressing capability, is studied and analyzed. The system utilizes one level of indexing and the database is clustered. The logic-per-track approach is used for parallel processing of the data in a cylinder. The attributes and the tuples are allowed to have an arbitrary length and no encoding algorithm is used. The performance of the system is analyzed and it is demonstrated to have superior performance in comparison to software-based systems. The cost effectiveness of the system is also shown
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View based, global design of access structures for relational databases
In this dissertation we consider the problem of
automating the design of access structures for relational
database systems. The main considerations are effective
and rigorous utilization of the users' usage patterns,
global treatment of the whole design and utilizing most of
the commonly known access structures.
We represent the usage patterns on the access structures
as relational algebra views. We transform a view
into one or more simple access structures. Using the simple
access structures we generate compound access structures.
From this set of access structures we choose an
approximately optimal set of access structures that process
the input views as efficiently as possible and obey
the storage space constraint.
In developing the transformation methods, we also
develop the concepts of aggregation and generalization for
access structures, and use them in obtaining compound
access structures from simple data items and in integrating
several access structures into one access structure.
We introduced a general method that shows how any complex
access structure is formed from the simple data items
using aggregation.
In the transformations we consider insertion, deletion,
look-up, update, building and storage space costs of
access structures and/or views. The access structure
types employed are tree, chain, circular chain, pointer
array, cluster of tuples and sorting tuples in one or two
relations. The access structures associated with a relation
may cover different numbers of tuples.
For the optimization we separate all access structures
into pointer and placement types. We give a 0-1
knapsack problem based approximate optimization algorithm
to solve the pointer access structure optimization problem.
We formulate the placement access structure optimization
as a 0-1 integer programming problem. In the global
optimization algorithm, we utilize the placement and
pointer optimization algorithms and approximately compensate
for the interdependencies between the placement and
pointer access structure optimizations
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An investigation to study the feasibility of on-line bibliographic information retrieval system using an APP
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis reports an investigation on the feasibility study of a
searching mechanism using an APP suitable for an on-line bibliographic
retrieval, operation, especially for retrospective searches.
From the study of the searching methods used in the conventional
systems it is seen that elaborate file- and data- structures are
introduced to improve the response time of the system. These
consequently lead to software and hardware redundancies. To mask
these complexities of the system an expensive computer with higher
capabilities and more powerful instruction set is commonly used.
Thus the service of the systen becomes cost-ineffective.
On the other hand the primitive operations of a searching mechanism,
such as, association, domain selection, intersection and unions, are
the intrinsic features of an associative parallel processor. Therefore
it is important to establish the feasibility of an APP as a cost-effective
searching mechanise.
In this thesis a searching mechanism using an 'ON-THE-FLY' searching
technique has been proposed. The parallel search unit uses a Byte-oriented
VRL-APP for efficient character string processing.
At the time of undertaking this work the specification for neither the
retrieval systems nor the BO-VRL APP's were well established; hence a
two-phase investigation was originated. In the Phase I of the work a
bottom up approach was adopted to derive a formal and precise
specification for the BO-VRL-APP. During the Phase II of the work
a top-down approach was opted for the implementation of the searching
mechanism.
An experimental research vehicle has been developed to establish
the feasibility of an APP as a cost-effective searching mechanism.
Although rigorous proof of the feasibility has not been obtained,
the thesis establishes that the APP is well suited for on-line
bibligraphic information retrieval operations where substring searches
including boolean selection and threshold weights are efficiently
supported
An Introduction to Database Systems
This textbook introduces the basic concepts of database systems. These concepts are presented through numerous examples in modeling and design. The material in this book is geared to an introductory course in database systems offered at the junior or senior level of Computer Science. It could also be used in a first year graduate course in database systems, focusing on a selection of the advanced topics in the latter chapters
Data Structures & Algorithm Analysis in C++
This is the textbook for CSIS 215 at Liberty University.https://digitalcommons.liberty.edu/textbooks/1005/thumbnail.jp
Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter
In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuronās significance and allow growing and pruning of HBF neurons during sequential learning process. From engineerās perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF
Bioinspired metaheuristic algorithms for global optimization
This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions
Runtime support for load balancing of parallel adaptive and irregular applications
Applications critical to today\u27s engineering research often must make use of the increased memory and processing power of a parallel machine. While advances in architecture design are leading to more and more powerful parallel systems, the software tools needed to realize their full potential are in a much less advanced state. In particular, efficient, robust, and high-performance runtime support software is critical in the area of dynamic load balancing. While the load balancing of loosely synchronous codes, such as field solvers, has been studied extensively for the past 15 years, there exists a class of problems, known as asynchronous and highly adaptive , for which the dynamic load balancing problem remains open. as we discuss, characteristics of this class of problems render compile-time or static analysis of little benefit, and complicate the dynamic load balancing task immensely.;We make two contributions to this area of research. The first is the design and development of a runtime software toolkit, known as the Parallel Runtime Environment for Multi-computer Applications, or PREMA, which provides interprocessor communication, a global namespace, a framework for the implementation of customized scheduling policies, and several such policies which are prevalent in the load balancing literature. The PREMA system is designed to support coarse-grained domain decompositions with the goals of portability, flexibility, and maintainability in mind, so that developers will quickly feel comfortable incorporating it into existing codes and developing new codes which make use of its functionality. We demonstrate that the programming model and implementation are efficient and lead to the development of robust and high-performance applications.;Our second contribution is in the area of performance modeling. In order to make the most effective use of the PREMA runtime software, certain parameters governing its execution must be set off-line. Optimal values for these parameters may be determined through repeated executions of the target application; however, this is not always possible, particularly in large-scale environments and long-running applications. We present an analytic model that allows the user to quickly and inexpensively predict application performance and fine-tune applications built on the PREMA platform