Search results for: Genetic testing

Authors: George Karkavitsas, Maria Rangoussi

Abstract:

We present a genetic algorithm application to the problem of object registration (i.e., object detection, localization and recognition) in a class of medical images containing various types of blood cells. The genetic algorithm approach taken here is seen to be most appropriate for this type of image, due to the characteristics of the objects. Successful cell registration results on real life microscope images of blood cells show the potential of the proposed approach.

Keywords: Genetic algorithms, object registration, pattern recognition, blood cell microscope images.

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Authors: Indu Saini, Phool Singh, Vikas Malik

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A novel method based on Genetic Algorithm to solve the boundary value problems (BVPs) of the Falkner–Skan equation over a semi-infinite interval has been presented. In our approach, we use the free boundary formulation to truncate the semi-infinite interval into a finite one. Then we use the shooting method based on Genetic Algorithm to transform the BVP into initial value problems (IVPs). Genetic Algorithm is used to calculate shooting angle. The initial value problems arisen during shooting are computed by Runge-Kutta Fehlberg method. The numerical solutions obtained by the present method are in agreement with those obtained by previous authors.

Keywords: Boundary Layer Flow, Falkner–Skan equation, Genetic Algorithm, Shooting method.

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Authors: A. U. Haque, W. Asrar, A. A. Omar, E. Sulaeman, J. S Mohamed Ali

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Open jet testing is a valuable testing technique which provides the desired results with reasonable accuracy. It has been used in past for the airships and now has recently been applied for the hybrid ones, having more non-buoyant force coming from the wings, empennage and the fuselage. In the present review work, an effort has been done to review the challenges involved in open jet testing. In order to shed light on the application of this technique, the experimental results of two different configurations are presented. Although, the aerodynamic results of such vehicles are unique to its own design; however, it will provide a starting point for planning any future testing. Few important testing areas which need more attention are also highlighted. Most of the hybrid buoyant aerial vehicles are unconventional in shape and there experimental data is generated, which is unique to its own design.

Keywords: Open jet testing, aerodynamics, hybrid buoyant aerial vehicles, airships.

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Authors: K. S. Neelukumari, K. B. Jayanthi

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In this paper genetic based test data compression is targeted for improving the compression ratio and for reducing the computation time. The genetic algorithm is based on extended pattern run-length coding. The test set contains a large number of X value that can be effectively exploited to improve the test data compression. In this coding method, a reference pattern is set and its compatibility is checked. For this process, a genetic algorithm is proposed to reduce the computation time of encoding algorithm. This coding technique encodes the 2n compatible pattern or the inversely compatible pattern into a single test data segment or multiple test data segment. The experimental result shows that the compression ratio and computation time is reduced.

Keywords: Backtracking, test data compression (TDC), x-filling, x-propagating and genetic algorithm.

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Authors: Marcos Devaner, Marcela Alves, Cledson Braga, Fabiano Alves, Wilton Bezerra

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This article discusses the inclusion of people with disabilities in the process of testing an accessible system solution for distance education. The accessible system, team profile, methodologies and techniques covered in the testing process are presented. The testing process shown in this paper was designed from the experience with user. The testing process emerged from lessons learned from past experiences and the end user is present at all stages of the tests. Also, lessons learned are reported and how it was possible the maturing of the team and the methods resulting in a simple, productive and effective process.

Keywords: Experience report, accessible systems, software testing, testing process, systems, e-learning.

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Authors: P. Sateesh Kumar , S. Ramachandram

Abstract:

Genetic Zone Routing Protocol (GZRP) is a new hybrid routing protocol for MANETs which is an extension of ZRP by using Genetic Algorithm (GA). GZRP uses GA on IERP and BRP parts of ZRP to provide a limited set of alternative routes to the destination in order to load balance the network and robustness during node/link failure during the route discovery process. GZRP is studied for its performance compared to ZRP in many folds like scalability for packet delivery and proved with improved results. This paper presents the results of the effect of load balancing on GZRP. The results show that GZRP outperforms ZRP while balancing the load.

Keywords: MANET, routing, ZRP, Genetic algorithm, GZRP, load balancing

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Authors: M. Analoui, M. Fadavi Amiri

Abstract:

The design of a pattern classifier includes an attempt to select, among a set of possible features, a minimum subset of weakly correlated features that better discriminate the pattern classes. This is usually a difficult task in practice, normally requiring the application of heuristic knowledge about the specific problem domain. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allow higher classification accuracy. Many current feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and increasing classification efficiency, it does not necessarily reduce the number of features that must be measured since each new feature may be a linear combination of all of the features in the original pattern vector. In this paper a new approach is presented to feature extraction in which feature selection, feature extraction, and classifier training are performed simultaneously using a genetic algorithm. In this approach each feature value is first normalized by a linear equation, then scaled by the associated weight prior to training, testing, and classification. A knn classifier is used to evaluate each set of feature weights. The genetic algorithm optimizes a vector of feature weights, which are used to scale the individual features in the original pattern vectors in either a linear or a nonlinear fashion. By this approach, the number of features used in classifying can be finely reduced.

Keywords: Feature reduction, genetic algorithm, pattern classification, nearest neighbor rule classifiers (k-NNR).

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Authors: Manpreet Singh, Keerat Kaur, Bhavdeep Singh

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With the hardware technology advancing, the cost of storing is decreasing. Thus there is an urgent need for new techniques and tools that can intelligently and automatically assist us in transferring this data into useful knowledge. Different techniques of data mining are developed which are helpful for handling these large size databases [7]. Data mining is also finding its role in the field of biotechnology. Pedigree means the associated ancestry of a crop variety. Genetic diversity is the variation in the genetic composition of individuals within or among species. Genetic diversity depends upon the pedigree information of the varieties. Parents at lower hierarchic levels have more weightage for predicting genetic diversity as compared to the upper hierarchic levels. The weightage decreases as the level increases. For crossbreeding, the two varieties should be more and more genetically diverse so as to incorporate the useful characters of the two varieties in the newly developed variety. This paper discusses the searching and analyzing of different possible pairs of varieties selected on the basis of morphological characters, Climatic conditions and Nutrients so as to obtain the most optimal pair that can produce the required crossbreed variety. An algorithm was developed to determine the genetic diversity between the selected wheat varieties. Cluster analysis technique is used for retrieving the results.

Keywords: Genetic diversity, pedigree, nutrients.

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Authors: L. Abdelmalek, M. Zerikat, M. Rahli

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In this paper, we present a comparative study of the genetic algorithms and Hessian-s methods for optimal research of the active powers in an electric network of power. The objective function which is the performance index of production of electrical energy is minimized by satisfying the constraints of the equality type and inequality type initially by the Hessian-s methods and in the second time by the genetic Algorithms. The results found by the application of AG for the minimization of the electric production costs of power are very encouraging. The algorithms seem to be an effective technique to solve a great number of problems and which are in constant evolution. Nevertheless it should be specified that the traditional binary representation used for the genetic algorithms creates problems of optimization of management of the large-sized networks with high numerical precision.

Keywords: Genetic algorithm, Flow of optimum loadimpedances, Hessians method, Optimal distribution.

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Authors: Qin Ding, Jim Gasvoda

Abstract:

Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for clustering on very large data sets, especially on image data sets. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. We test our algorithm on both artificial and real image data sets, both of which are of large size. The experimental results show that our algorithm outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.

Keywords: Clustering, data mining, genetic algorithm, image data.

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Authors: Manisha Dubey, Aalok Dubey

Abstract:

This paper presents an approach for the design of fuzzy logic power system stabilizers using genetic algorithms. In the proposed fuzzy expert system, speed deviation and its derivative have been selected as fuzzy inputs. In this approach the parameters of the fuzzy logic controllers have been tuned using genetic algorithm. Incorporation of GA in the design of fuzzy logic power system stabilizer will add an intelligent dimension to the stabilizer and significantly reduces computational time in the design process. It is shown in this paper that the system dynamic performance can be improved significantly by incorporating a genetic-based searching mechanism. To demonstrate the robustness of the genetic based fuzzy logic power system stabilizer (GFLPSS), simulation studies on multimachine system subjected to small perturbation and three-phase fault have been carried out. Simulation results show the superiority and robustness of GA based power system stabilizer as compare to conventionally tuned controller to enhance system dynamic performance over a wide range of operating conditions.

Keywords: Dynamic stability, Fuzzy logic power systemstabilizer, Genetic Algorithms, Genetic based power systemstabilizer

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Authors: A. A. Hossam-Eldin, E. N. Abdallah, M. S. El-Nozahy

Abstract:

Power system state estimation is the process of calculating a reliable estimate of the power system state vector composed of bus voltages' angles and magnitudes from telemetered measurements on the system. This estimate of the state vector provides the description of the system necessary for the operation and security monitoring. Many methods are described in the literature for solving the state estimation problem, the most important of which are the classical weighted least squares method and the nondeterministic genetic based method; however both showed drawbacks. In this paper a modified version of the genetic algorithm power system state estimation is introduced, Sensitivity of the proposed algorithm to genetic operators is discussed, the algorithm is applied to case studies and finally it is compared with the classical weighted least squares method formulation.

Keywords: Genetic algorithms, ill-conditioning, state estimation, weighted least squares.

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Authors: J. Kaabi, Y. Harrath

Abstract:

This paper studied the flow shop scheduling problem under machine availability constraints. The machines are subject to flexible preventive maintenance activities. The nonresumable scenario for the jobs was considered. That is, when a job is interrupted by an unavailability period of a machine it should be restarted from the beginning. The objective is to minimize the total tardiness time for the jobs and the advance/tardiness for the maintenance activities. To solve the problem, a genetic algorithm was developed and successfully tested and validated on many problem instances. The computational results showed that the new genetic algorithm outperforms another earlier proposed algorithm. 

Keywords: Flow shop scheduling, maintenance, genetic algorithm, priority rules.

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Authors: G. Bago, P. Hedbávný, M. Kalichová

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The aim of the contribution is to project and consequently verify a testing battery which in practice would facilitate the selection of talented gymnasts for current concept of men´ s gymnastics. Based on study of professional literature a test array consisting of three parts projected – power testing, speed testing and flexibility testing– was projected. The evaluating scales used in the tests are standardized. This test array was applied to girls aged 6 - 7 during recruitment for Sokol Brno I. and SG Pelhrimov Gymnastic Club. After 6 months of training activity the projected set of tests was applied again. The results were evaluated through observation and questionnaire and they were consequently transformed into charts. Recommendation for practice was proposed based on these results.

Keywords: Talent selection, sports gymnastics, power testing, speed testing, flexibility testing.

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Authors: Robersy Sánchez, Ricardo Grau

Abstract:

A plausible architecture of an ancient genetic code is derived from an extended base triplet vector space over the Galois field of the extended base alphabet {D, G, A, U, C}, where the letter D represent one or more hypothetical bases with unspecific pairing. We hypothesized that the high degeneration of a primeval genetic code with five bases and the gradual origin and improvements of a primitive DNA repair system could make possible the transition from the ancient to the modern genetic code. Our results suggest that the Watson-Crick base pairing and the non-specific base pairing of the hypothetical ancestral base D used to define the sum and product operations are enough features to determine the coding constraints of the primeval and the modern genetic code, as well as the transition from the former to the later. Geometrical and algebraic properties of this vector space reveal that the present codon assignment of the standard genetic code could be induced from a primeval codon assignment. Besides, the Fourier spectrum of the extended DNA genome sequences derived from the multiple sequence alignment suggests that the called period-3 property of the present coding DNA sequences could also exist in the ancient coding DNA sequences.

Keywords: Genetic code vector space, primeval genetic code, power spectrum.

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Authors: Mubina Nazmeen, Iram Rubab

Abstract:

Formal Specification languages are being widely used for system specification and testing. Highly critical systems such as real time systems, avionics, and medical systems are represented using Formal specification languages. Formal specifications based testing is mostly performed using black box testing approaches thus testing only the set of inputs and outputs of the system. The formal specification language such as VDMµ can be used for white box testing as they provide enough constructs as any other high level programming language. In this work, we perform data and control flow analysis of VDMµ class specifications. The proposed work is discussed with an example of SavingAccount.

Keywords: VDM-SL, VDMµ, data flow graph, control flowgraph, testing, formal specification.

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Authors: R. C. Joshi, Shashikala Tapaswi

Abstract:

The paper proposes an approach using genetic algorithm for computing the region based image similarity. The image is denoted using a set of segmented regions reflecting color and texture properties of an image. An image is associated with a family of image features corresponding to the regions. The resemblance of two images is then defined as the overall similarity between two families of image features, and quantified by a similarity measure, which integrates properties of all the regions in the images. A genetic algorithm is applied to decide the most plausible matching. The performance of the proposed method is illustrated using examples from an image database of general-purpose images, and is shown to produce good results.

Keywords: Image Features, color descriptor, segmented classes, texture descriptors, genetic algorithm.

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Authors: Radhwan Yousif Sedik Al-Jawadi

Abstract:

Optimization is necessary for finding appropriate solutions to a range of real-life problems. In particular, genetic (or more generally, evolutionary) algorithms have proved very useful in solving many problems for which analytical solutions are not available. In this paper, we present an optimization algorithm called Dynamic Schema with Dissimilarity and Similarity of Chromosomes (DSDSC) which is a variant of the classical genetic algorithm. This approach constructs new chromosomes from a schema and pairs of existing ones by exploring their dissimilarities and similarities. To show the effectiveness of the algorithm, it is tested and compared with the classical GA, on 15 two-dimensional optimization problems taken from literature. We have found that, in most cases, our method is better than the classical genetic algorithm.

Keywords: Genetic algorithm, similarity and dissimilarity, chromosome injection, dynamic schema.

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Authors: Jarno Haapamäki, Juha Röning

Abstract:

Scale defects are common surface defects in hot steel rolling. The modelling of such defects is problematic and their causes are not straightforward. In this study, we investigated genetic algorithms in search for a mathematical solution to scale formation. For this research, a high-dimensional data set from hot steel rolling process was gathered. The synchronisation of the variables as well as the allocation of the measurements made on the steel strip were solved before the modelling phase.

Keywords: Genetic algorithms, hot strip rolling, knowledge discovery, modeling.

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Authors: Mallikarjun B. Channappagoudar, Pallapa Venkataram

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As a node monitoring protocol, which is a part of network management, operates in distributed manner, conformance testing of such protocols is more tedious than testing a peer-to-peer protocol. Various works carried out to give the methodology to do conformance testing of distributed protocol. In this paper, we have presented a formal approach for conformance testing of a Node Monitoring Protocol, which uses both static and mobile agents, for MANETs. First, we use SDL to obtain MSCs, which represent the scenario descriptions by sequence diagrams, which in turn generate test sequences and test cases. Later, Testing and Test Control Notation Version-3 (TTCN-3) is used to execute test cases with respect to generated test sequences to know the conformance of protocol against the given specification. This approach shows, the effective conformance testing of the distributed protocols for the network with varying node density and complex behavior. Experimental results for the protocol scenario represent the effectiveness of the method used.

Keywords: Conformance Testing, FSM, Mobile agent, TTCN, Test sequence.

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Authors: Bin Cai, Shilong Wang, Haibo Hu

Abstract:

The job shop scheduling problem (JSSP) is well known as one of the most difficult combinatorial optimization problems. This paper presents a hybrid genetic algorithm for the JSSP with the objective of minimizing makespan. The efficiency of the genetic algorithm is enhanced by integrating it with a local search method. The chromosome representation of the problem is based on operations. Schedules are constructed using a procedure that generates full active schedules. In each generation, a local search heuristic based on Nowicki and Smutnicki-s neighborhood is applied to improve the solutions. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

Keywords: Genetic algorithm, Job shop scheduling problem, Local search, Meta-heuristic algorithm

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Authors: A. Omran, G. El-Bayiumi, M. Bayoumi, A. Kassem

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Applicability of tuning the controller gains for Stewart manipulator using genetic algorithm as an efficient search technique is investigated. Kinematics and dynamics models were introduced in detail for simulation purpose. A PD task space control scheme was used. For demonstrating technique feasibility, a Stewart manipulator numerical-model was built. A genetic algorithm was then employed to search for optimal controller gains. The controller was tested onsite a generic circular mission. The simulation results show that the technique is highly convergent with superior performance operating for different payloads.

Keywords: Stewart kinematics, Stewart dynamics, task space control, genetic algorithm.

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Authors: S. Swain, P. S Khuntia

Abstract:

In this paper, the unstable angle of attack of a FOXTROT aircraft is controlled by using Genetic Algorithm based flight controller and the result is compared with the conventional techniques like Tyreus-Luyben (TL), Ziegler-Nichols (ZN) and Interpolation Rule (IR) for tuning the PID controller. In addition, the performance indices like Mean Square Error (MSE), Integral Square Error (ISE), and Integral Absolute Time Error (IATE) etc. are improved by using Genetic Algorithm. It was established that the error by using GA is very less as compared to the conventional techniques thereby improving the performance indices of the dynamic system.

Keywords: Angle of Attack, Genetic Algorithm, Performance Indices, PID Controller.

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Authors: Tao Zhao, Xin Wang

Abstract:

With the increasing complexity of engineering problems, the traditional, single-objective and deterministic optimization method can not meet people-s requirements. A multi-objective fuzzy optimization model of resource input is built for M chlor-alkali chemical eco-industrial park in this paper. First, the model is changed into the form that can be solved by genetic algorithm using fuzzy theory. And then, a fitness function is constructed for genetic algorithm. Finally, a numerical example is presented to show that the method compared with traditional single-objective optimization method is more practical and efficient.

Keywords: Fitness function, genetic algorithm, multi-objectivefuzzy optimization, satisfaction degree membership function.

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Authors: H.-G. Herrmann, M. Schwarz, J. Summa, F. Grossmann

Abstract:

In this work, different non-destructive testing methods for the characterization of defects and interfaces are presented. It is shown that, by means of active thermography, defects in the interface and in the carbon fiber reinforced polymer (CFRP) itself can be detected and determined. The bonding of metal and thermoplastic can be characterized very well by ultrasonic testing with electromagnetic acoustic transducers (EMAT). Mechanical testing is combined with passive thermography to correlate mechanical values with the defect-size. There is also a comparison between active and passive thermography. Mechanical testing shows the influence of different defects. Furthermore, a correlation of defect-size and loading to rupture was performed.

 

Keywords: Defect evaluation, EMAT, mechanical testing, thermography.

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Authors: N. Javadian, J. Rezaeian, Y. Maali

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In this paper a multi-objective nonlinear programming model of cellular manufacturing system is presented which minimize the intercell movements and maximize the sum of reliability of cells. We present a genetic approach for finding efficient solutions to the problem of cell formation for products having multiple routings. These methods find the non-dominated solutions and according to decision makers prefer, the best solution will be chosen.

Keywords: Cellular Manufacturing, Genetic Algorithm, Multiobjective, Life-Cycle.

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Authors: H. El-Madbouly

Abstract:

In this paper, genetic algorithm (GA) is proposed for the design of an optimization algorithm to achieve the bandwidth allocation of ATM network. In Broadband ISDN, the ATM is a highbandwidth; fast packet switching and multiplexing technique. Using ATM it can be flexibly reconfigure the network and reassign the bandwidth to meet the requirements of all types of services. By dynamically routing the traffic and adjusting the bandwidth assignment, the average packet delay of the whole network can be reduced to a minimum. M/M/1 model can be used to analyze the performance.

Keywords: Bandwidth allocation, Genetic algorithm, ATMNetwork, packet delay.

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Authors: J. Ramírez A., A. Rubiano F.

Abstract:

In this paper the direct kinematic model of a multiple applications three degrees of freedom industrial manipulator, was developed using the homogeneous transformation matrices and the Denavit - Hartenberg parameters, likewise the inverse kinematic model was developed using the same method, verifying that in the workload border the inverse kinematic presents considerable errors, therefore a genetic algorithm was implemented to optimize the model improving greatly the efficiency of the model.

Keywords: Direct Kinematic, Genetic Algorithm, InverseKinematic, Optimization, Robot Manipulator

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Authors: Zohreh Sheikh Khozani, Wan Hanna Melini Wan Mohtar, Mojtaba Porhemmat

Abstract:

Prediction of wall shear stress in a rectangular channel, with non-homogeneous roughness distribution, was studied. Estimation of shear stress is an important subject in hydraulic engineering, since it affects the flow structure directly. In this study, the Genetic Algorithm Artificial (GAA) neural network is introduced as a hybrid methodology of the Artificial Neural Network (ANN) and modified Genetic Algorithm (GA) combination. This GAA method was employed to predict the wall shear stress. Various input combinations and transfer functions were considered to find the most appropriate GAA model. The results show that the proposed GAA method could predict the wall shear stress of open channels with high accuracy, by Root Mean Square Error (RMSE) of 0.064 in the test dataset. Thus, using GAA provides an accurate and practical simple-to-use equation.

Keywords: Artificial neural network, genetic algorithm, genetic programming, rectangular channel, shear stress.

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Authors: Manisha Dubey, Pankaj Gupta

Abstract:

This paper presents a systematic approach for the design of power system stabilizer using genetic algorithm and investigates the robustness of the GA based PSS. The proposed approach employs GA search for optimal setting of PSS parameters. The performance of the proposed GPSS under small and large disturbances, loading conditions and system parameters is tested. The eigenvalue analysis and nonlinear simulation results show the effectiveness of the GPSS to damp out the system oscillations. It is found tat the dynamic performance with the GPSS shows improved results, over conventionally tuned PSS over a wide range of operating conditions.

Keywords: Genetic Algorithm, Genetic power system stabilizer, Power system stabilizer, Small signal stability

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