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In this work an image retrieval system adaptable to user's interests by the use of relevance feedback via genetic algorithm is presented. The retrieval process is based on local similarity patterns. The goal of the genetic algorithm is to infer weights for regions and features that better translate the user's requirements producing better quality rankings. The genetic algorithm used has as its main...
Since the appearance in 1993, first approaching the Shannon limit, the Turbo Codes give a new direction for the channel encoding field, especially since they were adopted for multiple norms of telecommunications, such as deeper communication. To obtain an excellent performance it is necessary to design robust turbo code interleaver. We are investigating genetic algorithms as a promising optimization...
This paper presents a new algorithm for binarization of historical document images. It is adjusted to deal with the particular case of documents which are written in both sides of the paper. The digitization of these documents in general brings to the digital image this back-to-front interference. In order to deal with it, a thresholding algorithm is proposed based on color quantization by genetic...
This paper proposes a dynamic software testing framework, which is able to analyse the source code of a program, create the necessary data structures for automatic testing, such as control flow graphs, and generate a near to optimum set of test cases with reference to a test coverage criterion. The framework consists of two sub-systems: the first is a program analysis system that identifies the type...
Genetic algorithms are vastly used in development of rule based classifier systems in data mining. In such tasks, the rule base is usually a set oflf-Then rules and the rules are developed using an evolutionary trait. GA is usually a good solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. This paper presents a novel algorithm...
Currently, genetic algorithms (GA) are widely used in different optimization problems. One of the problems with GAs is tuning their parameters correctly as they can have a significant effect on GA's overall performance. Till now, different methods have been proposed for fine tuning these parameters. Many of these methods use fuzzy linguistic rules in order to find the correct parameters in each stage...
An adaptive boosting ensemble algorithm for classifying homogeneous distributed data streams is presented. The method builds an ensemble of classifiers by using Genetic Programming (GP) to inductively generate decision trees, each trained on different parts of the distributed training set. The approach adopts a co-evolutionary platform to support a cooperative model of GP. A change detection strategy,...
Distributed genetic algorithm (DGA) is one of the most promising choices among the optimization methods. In this paper we describe DGAFrame, alpha flexible framework for evolutionary computation, written in Java. DGAFrame executes GAs across a range of machines communicating through RMI network technology, allowing the implementation of portable, flexible GAs that use the island model approach. Each...
In this paper, we discuss the study and implementation of two alternative artificial intelligence methodologies in declarative scene synthesis. In order to achieve this, we employ two approaches towards the acquisition and exploitation of the desired qualitative aspects of the synthesis on behalf of the designer. Our work concerns the adaptation and application of a machine learning technique, as...
The software cost estimation process is one of the most critical managerial activities related to project planning, resource allocation and control. As software development is a highly dynamic procedure, the difficulty of providing accurate cost estimations tends to increase with development complexity. The inherent problems of the estimation process stem from its dependence on several complex variables,...
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