Search results for: genetic modification.

Authors: Byung-Wan Jo, Kwang-Won Yoon, Ji-Sun Choi

Abstract:

Load managing method on road became necessary since overloaded vehicles occur damage on road facilities and existing systems for preventing this damage still show many problems.Accordingly, efficient managing system for preventing overloaded vehicles could be organized by using the road itself as a scale by applying genetic algorithm to analyze the load and the drive information of vehicles.Therefore, this paper organized Ubiquitous sensor network system for development of intelligent overload vehicle regulation system, also in this study, to use the behavior of road, the transformation was measured by installing underground box type indoor model and indoor experiment was held using genetic algorithm. And we examined wireless possibility of overloaded vehicle regulation system through experiment of the transmission and reception distance.If this system will apply to road and bridge, might be effective for economy and convenience through establishment of U-IT system..

Keywords: Overload Vehicle. Genetic Algorithm, EmbeddedSystem, Wim Sensor, overload vehicle regulation

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Authors: O. Pop, C. Barbulescu, M. Nemes, St. Kilyeni

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The tracing methods determine the contribution the power system sources have in their supplying. These methods can be used to assess the transmission prices, but also to recover the transmission fixed cost. In this paper is presented the influence of the modification of commons structure has on the specific price of transfer and on active power losses. The authors propose a power losses allocation method, based on Kirschen-s method. The system operator must make use of a few basic principles about allocation. The only necessary information is the power flows on system branches and the modifications applied to power system buses. In order to illustrate this method, the 25-bus test system is used, elaborated within the Electrical Power Engineering Department, from Timisoara, Romania.

Keywords: Power systems, P-U bus, P-Q bus, loss allocation, traceability methods.

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Authors: Seyed Mehran Kazemi, Bahare Fatemi

Abstract:

Sudoku is a logic-based combinatorial puzzle game which is popular among people of different ages. Due to this popularity, computer softwares are being developed to generate and solve Sudoku puzzles with different levels of difficulty. Several methods and algorithms have been proposed and used in different softwares to efficiently solve Sudoku puzzles. Various search methods such as stochastic local search have been applied to this problem. Genetic Algorithm (GA) is one of the algorithms which have been applied to this problem in different forms and in several works in the literature. In these works, chromosomes with little or no information were considered and obtained results were not promising. In this paper, we propose a new way of applying GA to this problem which uses more-informed chromosomes than other works in the literature. We optimize the parameters of our GA using puzzles with different levels of difficulty. Then we use the optimized values of the parameters to solve various puzzles and compare our results to another GA-based method for solving Sudoku puzzles.

Keywords: Genetic algorithm, optimization, solving Sudoku puzzles, stochastic local search.

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Authors: M. Ketcham, S. Vongpradhip

Abstract:

In this paper, an innovative watermarking scheme for audio signal based on genetic algorithms (GA) in the discrete wavelet transforms is proposed. It is robust against watermarking attacks, which are commonly employed in literature. In addition, the watermarked image quality is also considered. We employ GA for the optimal localization and intensity of watermark. The watermark detection process can be performed without using the original audio signal. The experimental results demonstrate that watermark is inaudible and robust to many digital signal processing, such as cropping, low pass filter, additive noise.

Keywords: Intelligent Audio Watermarking, GeneticAlgorithm, DWT Domain.

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Authors: G. Parmar, R. Prasad, S. Mukherjee

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The authors present an algorithm for order reduction of linear dynamic systems using the combined advantages of stability equation method and the error minimization by Genetic algorithm. The denominator of the reduced order model is obtained by the stability equation method and the numerator terms of the lower order transfer function are determined by minimizing the integral square error between the transient responses of original and reduced order models using Genetic algorithm. The reduction procedure is simple and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. The proposed algorithm has also been extended for the order reduction of linear multivariable systems. Two numerical examples are solved to illustrate the superiority of the algorithm over some existing ones including one example of multivariable system.

Keywords: Genetic algorithm, Integral square error, Orderreduction, Stability equation method.

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Authors: Gayadhar Panda, P. K. Rautraya

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In this paper, an investigation into the use of modified Genetic Algorithm optimized SSSC based controller to aid damping of low frequency inter-area oscillations in power systems is presented. Controller design is formulated as a nonlinear constrained optimization problem and modified Genetic Algorithm (MGA) is employed to search for the optimal controller parameters. For evaluation of effectiveness and robustness of proposed controllers, the performance was tested on multi-machine system subjected to different disturbances, loading conditions and system parameter variations. Simulation results are presented to show the fine performance of the proposed SSSC controller in damping the critical modes without significantly deteriorating the damping characteristics of other modes in multi-machine power system.

Keywords: SSSC, FACTS, Controller Design, Damping of Oscillations, Multi-machine system, Modified Genetic Algorithm (MGA).

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Authors: O. S. Ebrahim, M. A. Badr, Kh. H. Gharib, H. K. Temraz

Abstract:

This paper presents a methodology based on genetic algorithm (GA) to tune the parameters of proportional-integral-differential (PID) controllers utilized in the automatic voltage regulator (AVR) and speed governor of a brushless synchronous generator driven by three-stage steam turbine. The parameter tuning is represented as a nonlinear optimization problem solved by GA to minimize the integral of absolute error (IAE). The problem of integral windup due to physical system limitations is solved using simple anti-windup scheme. The obtained controllers are compared to those designed using classical Ziegler-Nichols technique and constrained optimization. Results show distinct superiority of the proposed method.

Keywords: Brushless synchronous generator, Genetic Algorithm, GA, Proportional-Integral-Differential control, PID control, automatic voltage regulator, AVR.

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Authors: Dawid Stawski, Silviya Halacheva, Dorota Zielińska

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The surface properties of many materials can be readily and predictably modified by the controlled deposition of thin layers containing appropriate functional groups and this research area is now a subject of widespread interest. The layer-by-layer (lbl) method involves depositing oppositely charged layers of polyelectrolytes onto the substrate material which are stabilized due to strong electrostatic forces between adjacent layers. This type of modification affords products that combine the properties of the original material with the superficial parameters of the new external layers. Through an appropriate selection of the deposited layers, the surface properties can be precisely controlled and readily adjusted in order to meet the requirements of the intended application. In the presented paper a variety of anionic (poly(acrylic acid)) and cationic (linear poly(ethylene imine), polymers were successfully deposited onto the polypropylene nonwoven using the lbl technique. The chemical structure of the surface before and after modification was confirmed by reflectance FTIR spectroscopy, volumetric analysis and selective dyeing tests. As a direct result of this work, new materials with greatly improved properties have been produced. For example, following a modification process significant changes in the electrostatic activity of a range of novel nanocomposite materials were observed. The deposition of polyelectrolyte nanolayers was found to strongly accelerate the loss of electrostatically generated charges and to increase considerably the thermal resistance properties of the modified fabric (the difference in T50% is over 20oC). From our results, a clear relationship between the type of polyelectrolyte layer deposited onto the flat fabric surface and the properties of the modified fabric was identified.

Keywords: Layer-by-layer technique, polypropylene nonwoven, surface modification, surface properties.

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Authors: M. Cermak, T. Karasek, J. Rojicek

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The key role in phenomenological modelling of cyclic plasticity is good understanding of stress-strain behaviour of given material. There are many models describing behaviour of materials using numerous parameters and constants. Combination of individual parameters in those material models significantly determines whether observed and predicted results are in compliance. Parameter identification techniques such as random gradient, genetic algorithm and sensitivity analysis are used for identification of parameters using numerical modelling and simulation. In this paper genetic algorithm and sensitivity analysis are used to study effect of 4 parameters of modified AbdelKarim-Ohno cyclic plasticity model. Results predicted by Finite Element (FE) simulation are compared with experimental data from biaxial ratcheting test with semi-elliptical loading path.

Keywords: Genetic algorithm, sensitivity analysis, inverse approach, finite element method, cyclic plasticity, ratcheting.

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Authors: Li-Yeh Chuang, Yu-Jen Hou, Jr., Cheng-Hong Yang

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Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, research is limited by the cost of genotyping the tremendous number of SNPs. Therefore, it is important to identify a small subset of informative SNPs, the so-called tag SNPs. This subset consists of selected SNPs of the genotypes, and accurately represents the rest of the SNPs. Furthermore, an effective evaluation method is needed to evaluate prediction accuracy of a set of tag SNPs. In this paper, a genetic algorithm (GA) is applied to tag SNP problems, and the K-nearest neighbor (K-NN) serves as a prediction method of tag SNP selection. The experimental data used was taken from the HapMap project; it consists of genotype data rather than haplotype data. The proposed method consistently identified tag SNPs with considerably better prediction accuracy than methods from the literature. At the same time, the number of tag SNPs identified was smaller than the number of tag SNPs in the other methods. The run time of the proposed method was much shorter than the run time of the SVM/STSA method when the same accuracy was reached.

Keywords: Genetic Algorithm (GA), Genotype, Single nucleotide polymorphism (SNP), tag SNPs.

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Authors: Viraj Tyagi, Ajai Jain, P. K. Jain, Aarushi Jain

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This paper presents a genetic algorithm based loading methodology for a capacity constrained job-shop with the consideration of alternative process plans for each part to be produced. Performance analysis of the proposed methodology is carried out for two case studies by considering two different manufacturing scenarios. Results obtained indicate that the methodology is quite effective in improving the shop load balance, and hence, it can be included in the frameworks of manufacturing planning systems of job-shop oriented industries.

Keywords: Manufacturing planning, loading, genetic algorithm, Job-Shop

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Authors: R. N. Patel

Abstract:

This paper deals with the tuning of parameters for Automatic Generation Control (AGC). A two area interconnected hydrothermal system with PI controller is considered. Genetic Algorithm (GA) and Particle Swarm optimization (PSO) algorithms have been applied to optimize the controller parameters. Two objective functions namely Integral Square Error (ISE) and Integral of Time-multiplied Absolute value of the Error (ITAE) are considered for optimization. The effectiveness of an objective function is considered based on the variation in tie line power and change in frequency in both the areas. MATLAB/SIMULINK was used as a simulation tool. Simulation results reveal that ITAE is a better objective function than ISE. Performances of optimization algorithms are also compared and it was found that genetic algorithm gives better results than particle swarm optimization algorithm for the problems of AGC.

Keywords: Area control error, Artificial intelligence, Automatic generation control, Genetic Algorithms and modeling, ISE, ITAE, Particle swarm optimization.

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Authors: Daniel Rivero, Julián Dorado, Juan R. Rabuñal, Alejandro Pazos, Javier Pereira

Abstract:

The development of Artificial Neural Networks (ANNs) is usually a slow process in which the human expert has to test several architectures until he finds the one that achieves best results to solve a certain problem. This work presents a new technique that uses Genetic Programming (GP) for automatically generating ANNs. To do this, the GP algorithm had to be changed in order to work with graph structures, so ANNs can be developed. This technique also allows the obtaining of simplified networks that solve the problem with a small group of neurons. In order to measure the performance of the system and to compare the results with other ANN development methods by means of Evolutionary Computation (EC) techniques, several tests were performed with problems based on some of the most used test databases. The results of those comparisons show that the system achieves good results comparable with the already existing techniques and, in most of the cases, they worked better than those techniques.

Keywords: Artificial Neural Networks, Evolutionary Computation, Genetic Programming.

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Authors: César Estébanez, Ricardo Aler, José M. Valls

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In this paper we present a GP-based method for automatically evolve projections, so that data can be more easily classified in the projected spaces. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. Fitness of each projection measures how easy is to classify the dataset after applying the projection. This is quickly computed by a Simple Linear Perceptron. We have tested our approach in three domains. The experiments show that it obtains good results, compared to other Machine Learning approaches, while reducing dimensionality in many cases.

Keywords: Classification, genetic programming, projections.

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Authors: K. Shakeri, M. Mohebbi

Abstract:

In order to consider the effects of the higher modes in the pushover analysis, during the recent years several multi-modal pushover procedures have been presented. In these methods the response of the considered modes are combined by the square-rootof- sum-of-squares (SRSS) rule while application of the elastic modal combination rules in the inelastic phases is no longer valid. In this research the feasibility of defining an efficient alternative combination method is investigated. Two steel moment-frame buildings denoted SAC-9 and SAC-20 under ten earthquake records are considered. The nonlinear responses of the structures are estimated by the directed algebraic combination of the weighted responses of the separate modes. The weight of the each mode is defined so that the resulted response of the combination has a minimum error to the nonlinear time history analysis. The genetic algorithm (GA) is used to minimize the error and optimize the weight factors. The obtained optimal factors for each mode in different cases are compared together to find unique appropriate weight factors for each mode in all cases.

Keywords: Genetic Algorithm, Modal Pushover, Optimalweight.

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Authors: Bin Mu, Site Li, Shijin Yuan

Abstract:

Under the circumstance of environment deterioration, people are increasingly concerned about the quality of the environment, especially air quality. As a result, it is of great value to give accurate and timely forecast of AQI (air quality index). In order to simplify influencing factors of air quality in a city, and forecast the city’s AQI tomorrow, this study used MATLAB software and adopted the method of constructing a mathematic model of PCA-GABP to provide a solution. To be specific, this study firstly made principal component analysis (PCA) of influencing factors of AQI tomorrow including aspects of weather, industry waste gas and IAQI data today. Then, we used the back propagation neural network model (BP), which is optimized by genetic algorithm (GA), to give forecast of AQI tomorrow. In order to verify validity and accuracy of PCA-GABP model’s forecast capability. The study uses two statistical indices to evaluate AQI forecast results (normalized mean square error and fractional bias). Eventually, this study reduces mean square error by optimizing individual gene structure in genetic algorithm and adjusting the parameters of back propagation model. To conclude, the performance of the model to forecast AQI is comparatively convincing and the model is expected to take positive effect in AQI forecast in the future.

Keywords: AQI forecast, principal component analysis, genetic algorithm, back propagation neural network model.

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Authors: Tsuneyuki Namekata, Yoko Namekata

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It is known that the mean investment evolves from a very low initial value to some high level in the Continuous Prisoner's Dilemma. We examine how the cooperation level evolves from a low initial level to a high level in our Demographic Multi-level Donor-Recipient situation. In the Multi-level Donor-Recipient game, one player is selected as a Donor and the other as a Recipient randomly. The Donor has multiple cooperative moves and one defective move. A cooperative move means the Donor pays some cost for the Recipient to receive some benefit. The more cooperative move the Donor takes, the higher cost the Donor pays and the higher benefit the Recipient receives. The defective move has no effect on them. Two consecutive Multi-level Donor-Recipient games, one as a Donor and the other as a Recipient, can be viewed as a discrete version of the Continuous Prisoner's Dilemma. In the Demographic Multi-level Donor-Recipient game, players are initially distributed spatially. In each period, players play multiple Multi-level Donor-Recipient games against other players. He leaves offspring if possible and dies because of negative accumulated payoff of him or his lifespan. Cooperative moves are necessary for the survival of the whole population. There is only a low level of cooperative move besides the defective move initially available in strategies of players. A player may modify and expand his strategy by his recent experiences or practices. We distinguish several types of a player about modification and expansion. We show, by Agent-Based Simulation, that introducing only the modification increases the emergence rate of cooperation and introducing both the modification and the expansion further increases it and a high level of cooperation does emerge in our Demographic Multi-level Donor-Recipient Game.

Keywords: Agent-based simulation, donor-recipient game, emergence of cooperation, spatial structure, TFT, TF2T.

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Authors: R. Parameshwaran, R. Karunakaran, S. Iniyan, Anand A. Samuel

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The objective of this study is to present the test results of variable air volume (VAV) air conditioning system optimized by two objective genetic algorithm (GA). The objective functions are energy savings and thermal comfort. The optimal set points for fuzzy logic controller (FLC) are the supply air temperature (Ts), the supply duct static pressure (Ps), the chilled water temperature (Tw), and zone temperature (Tz) that is taken as the problem variables. Supply airflow rate and chilled water flow rate are considered to be the constraints. The optimal set point values are obtained from GA process and assigned into fuzzy logic controller (FLC) in order to conserve energy and maintain thermal comfort in real time VAV air conditioning system. A VAV air conditioning system with FLC installed in a software laboratory has been taken for the purpose of energy analysis. The total energy saving obtained in VAV GA optimization system with FLC compared with constant air volume (CAV) system is expected to achieve 31.5%. The optimal duct static pressure obtained through Genetic fuzzy methodology attributes to better air distribution by delivering the optimal quantity of supply air to the conditioned space. This combination enhanced the advantages of uniform air distribution, thermal comfort and improved energy savings potential.

Keywords: Energy savings, fuzzy logic, Genetic algorithm, Thermal Comfort

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Authors: Kittaya Somsai, Thanatchai Kulworawanichpong, Nitus Voraphonpiput

Abstract:

This paper presents the DC voltage control design of D-STATCOM when the D-STATCOM is used for load voltage regulation. Although, the DC voltage can be controlled by active current of the D-STATCOM, reactive current still affects the DC voltage. To eliminate this effect, the control strategy with elimination effect of the reactive current is proposed and the results of the control with and without the elimination the effect of the reactive current are compared. For obtaining the proportional and integral gains of the PI controllers, the symmetrical optimum and genetic algorithms methods are applied. The stability margin of these methods are obtained and discussed in detail. In addition, the performance of the DC voltage control based on symmetrical optimum and genetic algorithms methods are compared. Effectiveness of the controllers designed was verified through computer simulation performed by using Power System Tool Block (PSB) in SIMULINK/MATLAB. The simulation results demonstrated that the DC voltage control proposed is effective in regulating DC voltage when the DSTATCOM is used for load voltage regulation.

Keywords: D-STATCOM, DC voltage control, Symmetrical optimum, Genetic algorithms

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Authors: Loai Nasrat, Mervat S. Hassan

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Bentonitic material from South Aswan, Egypt was evaluated in terms of mineral-ogy and chemical composition as bleaching clay in refining of transformer oil before and after acid activation and thermal treatment followed by acid leaching using HCl and H2SO4 for different contact times. Structural modification and refining power of bento-nite were investigated during modification by means of X-ray diffraction and infrared spectroscopy. The results revealed that the activated bentonite could be used for refining of transformer oil. The oil parameters such as; dielectric strength, viscosity and flash point had been improved. The dielectric breakdown strength of used oil increased from 29 kV for used oil treated with unactivated bentonite to 74 kV after treatment with activated bentonite. Kinematic Viscosity changed from 19 to 11 mm2 /s after treatment with activated bentonite. However, flash point achieved 149 ºC.

Keywords: Dielectric strength, unactivated bentonite, X-ray diffraction, SEM image

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Authors: Khalil Banan, Mohammad B.B. Sharifian, Jafar Mohammadi

Abstract:

Due to the high percentage of induction motors in industrial market, there exist a large opportunity for energy savings. Replacement of working induction motors with more efficient ones can be an important resource for energy savings. A calculation of energy savings and payback periods, as a result of such a replacement, based on nameplate motor efficiency or manufacture-s data can lead to large errors [1]. Efficiency of induction motors (IMs) can be extracted using some procedures that use the no-load test results. In the cases that we must estimate the efficiency on-line, some of these procedures can-t be efficient. In some cases the efficiency estimates using the rating values of the motor, but these procedures can have errors due to the different working condition of the motor. In this paper the efficiency of an IM estimated by using the genetic algorithm. The results are compared with the measured values of the torque and power. The results show smaller errors for this procedure compared with the conventional classical procedures, hence the cost of the equipments is reduced and on-line estimation of the efficiency can be made.

Keywords: Genetic algorithm, induction motor, efficiency.

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Authors: H. Alkhatib, J. Duveau

Abstract:

Genetic algorithms (GAs) have been widely used for global optimization problems. The GA performance depends highly on the choice of the search space for each parameter to be optimized. Often, this choice is a problem-based experience. The search space being a set of potential solutions may contain the global optimum and/or other local optimums. A bad choice of this search space results in poor solutions. In this paper, our approach consists in extending the search space boundaries during the GA optimization, only when it is required. This leads to more diversification of GA population by new solutions that were not available with fixed search space boundaries. So, these dynamic search spaces can improve the GA optimization performances. The proposed approach is applied to power system stabilizer optimization for multimachine power system (16-generator and 68-bus). The obtained results are evaluated and compared with those obtained by ordinary GAs. Eigenvalue analysis and nonlinear system simulation results show the effectiveness of the proposed approach to damp out the electromechanical oscillation and enhance the global system stability.

Keywords: Genetic Algorithms, Multiobjective Optimization, Power System Stabilizer, Small Signal Stability.

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Authors: Konstantinos C. Giotopoulos, Christos E. Alexakos, Grigorios N. Beligiannis, Spiridon D.Likothanassis

Abstract:

In this contribution an innovative platform is being presented that integrates intelligent agents and evolutionary computation techniques in legacy e-learning environments. It introduces the design and development of a scalable and interoperable integration platform supporting: I) various assessment agents for e-learning environments, II) a specific resource retrieval agent for the provision of additional information from Internet sources matching the needs and profile of the specific user and III) a genetic algorithm designed to extract efficient information (classifying rules) based on the students- answering input data. The agents are implemented in order to provide intelligent assessment services based on computational intelligence techniques such as Bayesian Networks and Genetic Algorithms. The proposed Genetic Algorithm (GA) is used in order to extract efficient information (classifying rules) based on the students- answering input data. The idea of using a GA in order to fulfil this difficult task came from the fact that GAs have been widely used in applications including classification of unknown data. The utilization of new and emerging technologies like web services allows integrating the provided services to any web based legacy e-learning environment.

Keywords: Bayesian Networks, Computational Intelligencetechniques, E-learning legacy systems, Service Oriented Integration, Intelligent Agents, Genetic Algorithms.

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Authors: M. Linek

Abstract:

This article presents the comparison of selected evaluation methods regarding microstructure modification of hardened cement concrete intended for airfield pavements. Basic test results were presented for two pavement quality concrete lots. Analysis included standard concrete used for airfield pavements and modern material solutions based on concrete composite modification. In case of basic grain size distribution of concrete cement CEM I 42,5HSR NA, fine aggregate and coarse aggregate fractions in the form of granite chippings, water and admixtures were considered. In case of grain size distribution of modified concrete, the use of modern modifier as substitute of fine aggregate was suggested. Modification influence on internal concrete structure parameters using scanning electron microscope was defined. Obtained images were compared to the results obtained using computed tomography. Opportunity to use this type of equipment for internal concrete structure diagnostics and an attempt of its parameters evaluation was presented. Obtained test results enabled to reach a conclusion that both methods can be applied for pavement quality concrete diagnostics, with particular purpose of airfield pavements.

Keywords: Scanning electron microscope, computed tomography, cement concrete, airfield pavements.

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Authors: Essa Abrahim Abdulgader Saleem, Thien-My Dao

Abstract:

The main objective of this paper is to optimize series-parallel system reliability using Genetic Algorithm (GA) and statistical analysis; considering system reliability constraints which involve the redundant numbers of selected components, total cost, and total weight. To perform this work, firstly the mathematical model which maximizes system reliability subject to maximum system cost and maximum system weight constraints is presented; secondly, a statistical analysis is used to optimize GA parameters, and thirdly GA is used to optimize series-parallel systems reliability. The objective is to determine the strategy choosing the redundancy level for each subsystem to maximize the overall system reliability subject to total cost and total weight constraints. Finally, the series-parallel system case study reliability optimization results are showed, and comparisons with the other previous results are presented to demonstrate the performance of our GA.

Keywords: Genetic algorithm, optimization, reliability, statistical analysis.

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Authors: Behnam Mehrkian, Arash Bahar, Ali Chaibakhsh

Abstract:

Magneto-rheological (MR) fluid damper is a semiactive control device that has recently received more attention by the vibration control community. But inherent hysteretic and highly nonlinear dynamics of MR fluid damper is one of the challenging aspects to employ its unique characteristics. The combination of artificial neural network (ANN) and fuzzy logic system (FLS) have been used to imitate more precisely the behavior of this device. However, the derivative-based nature of adaptive networks causes some deficiencies. Therefore, in this paper, a novel approach that employ genetic algorithm, as a free-derivative algorithm, to enhance the capability of fuzzy systems, is proposed. The proposed method used to model MR damper. The results will be compared with adaptive neuro-fuzzy inference system (ANFIS) model, which is one of the well-known approaches in soft computing framework, and two best parametric models of MR damper. Data are generated based on benchmark program by applying a number of famous earthquake records.

Keywords: Benchmark program, earthquake record filtering, fuzzy logic, genetic algorithm, MR damper.

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Authors: Cheng-Hong Yang, Tsung-Mu Shih, Li-Yeh Chuang

Abstract:

Serial Analysis of Gene Expression is a powerful quantification technique for generating cell or tissue gene expression data. The profile of the gene expression of cell or tissue in several different states is difficult for biologists to analyze because of the large number of genes typically involved. However, feature selection in machine learning can successfully reduce this problem. The method allows reducing the features (genes) in specific SAGE data, and determines only relevant genes. In this study, we used a genetic algorithm to implement feature selection, and evaluate the classification accuracy of the selected features with the K-nearest neighbor method. In order to validate the proposed method, we used two SAGE data sets for testing. The results of this study conclusively prove that the number of features of the original SAGE data set can be significantly reduced and higher classification accuracy can be achieved.

Keywords: Serial Analysis of Gene Expression, Feature selection, Genetic Algorithm, K-nearest neighbor method.

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Authors: Watchara Songserm, Teeradej Wuttipornpun

Abstract:

This paper presents a genetic algorithm based permutation and non-permutation scheduling heuristics (GAPNP) to solve a multi-stage finite capacity material requirement planning (FCMRP) problem in automotive assembly flow shop with unrelated parallel machines. In the algorithm, the sequences of orders are iteratively improved by the GA characteristics, whereas the required operations are scheduled based on the presented permutation and non-permutation heuristics. Finally, a linear programming is applied to minimize the total cost. The presented GAPNP algorithm is evaluated by using real datasets from automotive companies. The required parameters for GAPNP are intently tuned to obtain a common parameter setting for all case studies. The results show that GAPNP significantly outperforms the benchmark algorithm about 30% on average.

Keywords: Finite capacity MRP, genetic algorithm, linear programming, flow shop, unrelated parallel machines, application in industries.

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Authors: Emma K. Sales

Abstract:

Durian is the flagship fruit of Mindanao and there is an abundance of several cultivars with many confusing identities/ names. The project was conducted to develop procedure for reliable and rapid detection and sorting of durian planting materials. Moreover, it is also aimed to establish specific genetic or DNA markers for routine testing and authentication of durian cultivars in question. The project developed molecular procedures for routine testing. SSR primers were also screened and identified for their utility in discriminating durian cultivars collected. Results of the study showed the following accomplishments: 1. Twenty (29) SSR primers were selected and identified based on their ability to discriminate durian cultivars, 2. Optimized and established standard procedure for identification and authentication of Durian cultivars 3. Genetic profile of durian is now available at Biotech Unit Our results demonstrate the relevance of using molecular techniques in evaluating and identifying durian clones. The most polymorphic primers tested in this study could be useful tools for detecting variation even at the early stage of the plant especially for commercial purposes. The process developed combines the efficiency of the microsatellites development process with the optimization of non-radioactive detection process resulting in a user-friendly protocol that can be performed in two (2) weeks and easily incorporated into laboratories about to start microsatellite development projects. This can be of great importance to extend microsatellite analyses to other crop species where minimal genetic information is currently available. With this, the University can now be a service laboratory for routine testing and authentication of durian clones.

Keywords: DNA, SSR Analysis, genotype, genetic diversity, cultivars.

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Authors: Ali Asadian, Behzad Moshiri, Ali Khaki Sedigh, Caro Lucas

Abstract:

Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, a method using a hybrid-adaptive network based fuzzy inference system (ANFIS) has been proposed which is trained during the availability of GPS signal to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with respect to the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS integration in comparison with conventional ANFIS specially in the cases of satellites- outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.

Keywords: Adaptive Network based Fuzzy Inference System (ANFIS), Genetic optimization, Global Positioning System (GPS), Inertial Navigation System (INS).

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