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In this paper, we proposed a framework for detecting network's intrusions using Genetic Algorithm (GA) with multiple criteria. First of all, we build an intrusion detection system (IDS) using a pure GA with multiple selection methods. Then, we proposed one of the few hybrid algorithms in the literature, which is hybridized using a GA and a Tabu search (TS) algorithm. The proposed hybrid algorithm...
Distribution has a large portion of the total cost of sales of goods or product. It cause high cost transport and risk to the quality of the goods which are distributed, especially perishable product. Perishable product quality is greatly influenced by distribution, thus when the distribution not optimal, it will risk very big deal of broken product. In connection with this matter, the distribution...
This paper presents a hybrid test generation approach from extended finite state machines combining genetic algorithms with local search techniques. Many test generation methods (both functional and structural testing methods) use genetic algorithms. Genetic algorithms may take a long time to converge to a global optimum and for a huge neighborhood they can be inefficient or unsuccessful. In this...
Software testing is a very crucial and important phase for (SDLC) software development life cycle. Software is being tested on its effectiveness for generating good quality software. Regression testing is done by considering the constraints of resources and in this phase optimization of test suite is very important and crucial. This paper mainly aims to make use of hybrid approach of meta-heuristics,...
Software development and management of large scale projects are the complicated task. To support the software developers, the object oriented methodologies are used for reducing development efforts. But still a significant amount of efforts and team work is required to design and develop the systems according to the users need. Among them the testing is a one of the key components of software development...
A method of feature selection using elitist Genetic Algorithm is proposed in this work. Stratified-tenfold-cross-validation classification accuracy is used as fitness function. The method developed can detect redundant and irrelevant features, consequently producing the optimal feature set. The algorithm is carried out on the four benchmark datasets. Results of the experiments carried out shows that...
Various methods have been utilized for the cooperative tasking of unmanned aerial vehicles (UAVs), with the genetic algorithm (GA) being a technique that has proven to be versatile and effective for this use. The design and implementation of a GA is both an art and a science that brings together creativity, theoretical foundations and engineering. The focus of this paper is to show how the fitness...
Exhaustive testing is extremely difficult to perform owing to the large number of combinations. Thus, sampling and finding the optimal test suite from a set of feasible test cases becomes a central concern. Addressing this issue, the adoption of t-way testing (where t indicates the interaction strength) has come into the limelight. In order to summarize the achievements so far and facilitate future...
Software testing is an essential part of the SDLC(Software Development Life Cycle). Test scenarios are used to derive test cases for model based testing. However, with the software rapidly growing in size and complexity, the cost of software will be too high if we want to test all the test cases. So this paper presents an approach using Hybrid Genetic Algorithm(HGA) to prioritize test scenarios, which...
Software testing is one of the important stages of software development. In software development, developers always depend on testing to reveal bugs. In the maintenance stage test suite size grow because of integration of new technique. Addition of new technique force to create new test case which increase the size of test suite. In regression testing new test case may be added to the test suite during...
Pairwise testing is an effective combinatorial test generation technique that can generate relative small test suite to cover all pairs of parameter values at least once. Genetic algorithm has been used for pairwise test suite generation by some researchers. In order to improve the performance of genetic algorithm, this paper proposes a hybrid optimization algorithm by augmenting genetic algorithm...
The contribution presents automatic software-based self (SBST) test generation for processors as a basic blocks of current complex systems on chip and embedded systems. Processors testing can be extended by functional tests or using various application programs. Such types of tests are serving as additional tests to structural testing or as tests used in verification. Functional tests (programs) are...
Intelligent agents consist in a promising computing technology for the development of complex distributed systems. Despite the available theoretical references for guiding the designer of these agents, there are few proposed testing techniques to validate these systems. It's known that this validation depends on all the selected test cases, which should provide information regarding the components...
It is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. As the growth of computer networks is increasing day by day in modern society, therefore network security is one of the hottest issue to be solved. This paper proposes application of Genetic Algorithm (GA) for network intrusion detection system. Any action that...
This paper is based on Swine Influenza Models Based Optimization (SIMBO) natural computing technique which is used to automatic software test data generation. The concept of dominance relation between nodes has been used as fitness function in this paper. Finally, the results in the paper show the effectiveness of SIMBO techniques as compare to Particle Swarm Optimization (PSO) and Genetic Algorithm...
Regression test suites are necessary to ensure that changes to the system made after bug fixes or reimplementation have not corrupted the intended functionality. However, because of the complexity of current hardware systems, it is desirable to have optimized regression suites that provide the highest verification coverage with minimal simulation time and resources. In this paper, we introduce a coverage-directed...
Class imbalance problem refers to unequal distribution of data instances between classes. Due to this, popular classifiers misclassify data instances of minority class into majority class. Initially, Extreme learning machine was proposed with the prime objective of handling real valued datasets. Though, it a fast learning technique, it suffers from the drawback of misclassification of imbalanced dataset...
This paper describes a genetic algorithm used to solve the timetabling problem. Our approach will deal with the weekly curriculum based timetabling problem using real datasets from the University of Udine. Before proposing an algorithm we examine and compare the performance of the different genetic operators.
In this paper we propose a method and a tool to generate test suites from extended finite state machines, accounting for multiple (potentially conflicting) objectives. We aim at maximizing coverage and feasibility of a test suite while minimizing similarity between its test cases and minimizing overall cost. Therefore, we define a multi-objective genetic algorithm that searches for optimal test suites...
There is an increasing research interest in unmanned autonomous vehicles (UAVs) such as quadrotors. These researches applies these quadrotors for much more complicated tasks with most requiring cameras and GPS modules for positioning. This paper presents an alternative way of position localization of a quadrotor without the use of cameras and GPS modules by means of transceivers and Genetic Algorithm...
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