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Journal articles on the topic 'System of genetic algorithm'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

RAJALAKSHMI.M, RAJALAKSHMI M. "Software System Re-Modularization using Interactive Genetic Algorithm." Paripex - Indian Journal Of Research 3, no. 4 (January 15, 2012): 105–7. http://dx.doi.org/10.15373/22501991/apr2014/32.

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Vyas, Sanjay R., and Dr Ved Vyas Dwivedi. "Genetic Algorithm for Plant Generation Schedule in Electrical Power System." Paripex - Indian Journal Of Research 2, no. 1 (January 15, 2012): 52–53. http://dx.doi.org/10.15373/22501991/jan2013/19.

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3

Nabil, Emad, Amr Badr, and Ibrahim Farag. "An Immuno-Genetic Hybrid Algorithm." International Journal of Computers Communications & Control 4, no. 4 (December 1, 2009): 374. http://dx.doi.org/10.15837/ijccc.2009.4.2454.

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The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before them.

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Noshadi, Tayebe, Marzieh Dadvar, Nastaran Mirza, and Shima Shamseddini. "Adjust genetic algorithm parameter by fuzzy system." Ciência e Natura 37 (December 19, 2015): 190. http://dx.doi.org/10.5902/2179460x20771.

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Genetic algorithm is one of the random searches algorithm. Genetic algorithm is a method that uses genetic evolution as a model of problem solving. Genetic algorithm for selecting the best population, but the choices are not as heuristic information to be used in specific issues. In order to obtain optimal solutions and efficient use of fuzzy systems with heuristic rules that we would aim to increase the efficiency of parallel genetic algorithms using fuzzy logic immigration, which in fact do this by optimizing the parameters compared with the use of fuzzy system is done.

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Li, Xiaocong, Zhanying Wang, Junhua Xu, and Baochao Chen. "Power System Stabilizer Parameters Designing Based on Genetic Simulated Annealing Algorithm." Journal of Clean Energy Technologies 4, no. 3 (2015): 178–82. http://dx.doi.org/10.7763/jocet.2016.v4.275.

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6

Ohn, Syng-Yup, and Seung-Do Chi. "Cancer Diagnosis System using Genetic Algorithm and Multi-boosting Classifier." Journal of the Korea Society for Simulation 20, no. 2 (June 30, 2011): 77–85. http://dx.doi.org/10.9709/jkss.2011.20.2.077.

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ZHUANG, Jian, Qing-Yu YANG, Hai-Feng DU, and De-Hong YU. "High Efficient Complex System Genetic Algorithm." Journal of Software 21, no. 11 (January 28, 2011): 2790–801. http://dx.doi.org/10.3724/sp.j.1001.2010.03673.

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Zahradníková, Barbora, Soňa Duchovičová, and Peter Schreiber. "Facial Composite System Using Genetic Algorithm." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 22, no. 341 (December 1, 2014): 47–51. http://dx.doi.org/10.2478/rput-2014-0007.

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Abstract The article deals with genetic algorithms and their application in face identification. The purpose of the research is to develop a free and open-source facial composite system using evolutionary algorithms, primarily processes of selection and breeding. The initial testing proved higher quality of the final composites and massive reduction in the composites processing time. System requirements were specified and future research orientation was proposed in order to improve the results.

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9

Tam, S. "Genetic algorithm based defect identification system." Expert Systems with Applications 18, no. 1 (January 2000): 17–25. http://dx.doi.org/10.1016/s0957-4174(99)00046-9.

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10

Sukhija, Pratibha, Sunny Behal, and Pritpal Singh. "Face Recognition System Using Genetic Algorithm." Procedia Computer Science 85 (2016): 410–17. http://dx.doi.org/10.1016/j.procs.2016.05.183.

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Protopopova, Julia, and Sergey Kulik. "Educational Intelligent System Using Genetic Algorithm." Procedia Computer Science 169 (2020): 168–72. http://dx.doi.org/10.1016/j.procs.2020.02.130.

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12

Sarkar, Bikash Kanti, and Swapan Kumar Chakraborty. "Classification system using parallel genetic algorithm." International Journal of Innovative Computing and Applications 3, no. 4 (2011): 223. http://dx.doi.org/10.1504/ijica.2011.044569.

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13

Basal, G. P., Bhupendra Verma, A. K. Tiwari, and P. K. Chande. "Genetic Algorithm-based Fuzzy Expert System." IETE Technical Review 19, no. 3 (May 2002): 111–18. http://dx.doi.org/10.1080/02564602.2002.11417019.

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14

Kumon, Toshiro, Makoto Iwasaki, Tatsuya Suzuki, Tomonori Hashiyama, Nobuyuki Matsui, and Shigeru Okuma. "Nonlinear System Identification for Mechatronics Systems by Genetic Algorithm." IEEJ Transactions on Industry Applications 120, no. 11 (2000): 1343–50. http://dx.doi.org/10.1541/ieejias.120.1343.

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15

Cheng, Dong Mei, Chang Hua Qiu, and Cheng Yang Liu. "Structural Optimization Using Multi Evolutionary System Co-Exist Genetic Algorithm." Key Engineering Materials 450 (November 2010): 556–59. http://dx.doi.org/10.4028/www.scientific.net/kem.450.556.

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Traditional genetic algorithms put all the individuals in one population to cross and adopt the same set of evolutionary parameters and genetic operators to guide the evolution, which will easily lead to local convergence and poor searching efficiency. A multi evolutionary system co-exist genetic algorithm is developed to overcome the fluctuations of the whole evolution process through dividing individuals into several sub-populations according to the fitness value. Moreover, the improved algorithm prevents the early convergent and increases the diversity of individuals by supplying these sub-populations different evolutionary systems. The effectiveness and feasibility of the algorithm are verified by typical genetic algorithm test functions and an engineering case. The results show that the genetic algorithm has a good versatility, high convergence rate and solution precision.

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16

Kim, Shang-Hoon, and Sang-Yong Jung. "Optimal Design of Direct-Driven Wind Generator Using Genetic Algorithm Combined with Expert System." Journal of the Korean Institute of Illuminating and Electrical Installation Engineers 24, no. 10 (October 31, 2010): 149–56. http://dx.doi.org/10.5207/jieie.2010.24.10.149.

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17

Mitchell, Melanie, and Stephanie Forrest. "Genetic Algorithms and Artificial Life." Artificial Life 1, no. 3 (April 1994): 267–89. http://dx.doi.org/10.1162/artl.1994.1.3.267.

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Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, giving illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems. We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.

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18

Górski, Paweł, and Leszek Morzyński. "Active Noise Reduction Algorithm Based on NOTCH Filter and Genetic Algorithm." Archives of Acoustics 38, no. 2 (June 1, 2013): 185–90. http://dx.doi.org/10.2478/aoa-2013-0021.

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Abstract Application of active noise reduction (ANR) systems in hearing protectors requires the use of control algorithms to ensure stability of the ANR system and at the same time highly effective active noise reduction. A control algorithm based on NOTCH filters is an example of solutions that meet these criteria. Their disadvantage is operation over a narrow frequency band and a need for prior determination of frequencies to be reduced. This paper presents a solution of the ANR system for hearing protectors which is controlled with the use of modified NOTCH filters with parameters determined by a genetic algorithm. Application of a genetic algorithm allows to change the NOTCH filter reference signal frequency, and thus, adapt the filter to the reduced signal frequency.

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19

Hou, Caiping, and Xiyu Liu. "A New P System Based Genetic Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 1 (October 1, 2016): 165. http://dx.doi.org/10.11591/ijeecs.v4.i1.pp165-168.

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<p>For the “early convergence” or the “genetic drift” of the genetic algorithm, this paper proposes a new genetic algorithm based on P system. Based on the parallel mechanism of P system in membrane computing, we put forward the new P system based genetic algorithm (PBGA). So that we can improve the performance of GA.</p>

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20

Bano, Shaikh Sadaf. "Heart Disease Prediction System using Genetic Algorithm." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 2178–82. http://dx.doi.org/10.22214/ijraset.2019.6366.

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21

D., Nuka, and Emem E. "Traffic Light Control System using Genetic Algorithm." International Journal of Computer Applications 182, no. 22 (October 17, 2018): 37–43. http://dx.doi.org/10.5120/ijca2018918040.

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22

Todd, David S., Jaime A. Scott, and Pratyush Sen. "A Genetic Algorithm Approach to System Scheduling." IFAC Proceedings Volumes 31, no. 20 (July 1998): 277–82. http://dx.doi.org/10.1016/s1474-6670(17)41807-6.

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23

Ahn, Jong-Kap, Yun-Hyung Lee, Gang-Gyoo Jin, and Myung-Ok So. "System Identification by Real-Coded Genetic Algorithm." Journal of the Korean Society of Marine Engineering 31, no. 5 (July 31, 2007): 599–605. http://dx.doi.org/10.5916/jkosme.2007.31.5.599.

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24

Presbitero, Alva, Valeria Krzhizhanovskaya, Emiliano Mancini, Ruud Brands, and Peter Sloot. "Immune System Model Calibration by Genetic Algorithm." Procedia Computer Science 101 (2016): 161–71. http://dx.doi.org/10.1016/j.procs.2016.11.020.

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25

Lin, Chien Chuan, and Ming Shi Wang. "Genetic-clustering algorithm for intrusion detection system." International Journal of Information and Computer Security 2, no. 2 (2008): 218. http://dx.doi.org/10.1504/ijics.2008.018521.

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26

Hassan, Tariq A., Eman K. Ibrahim, and Ziad M. Abood. "Genetic Algorithm Filtering for Speaker Identification System." International Journal for Sciences and Technology 12, no. 1 (March 2017): 15–20. http://dx.doi.org/10.12816/0040714.

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27

Pham, D. T., and Y. Yang. "A Genetic Algorithm Based Preliminary Design System." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 207, no. 2 (April 1993): 127–33. http://dx.doi.org/10.1243/pime_proc_1993_207_170_02.

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The different stages in design are briefly discussed. Examples of previous research into automating the preliminary design stage are described. An architecture for a computer aided preliminary design system is proposed. A prototype system for generating design concepts for transmission devices is presented.

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28

Sarkar, Bikash Kanti, Shib Sankar Sana, and Kripasindhu Chaudhuri. "A genetic algorithm-based rule extraction system." Applied Soft Computing 12, no. 1 (January 2012): 238–54. http://dx.doi.org/10.1016/j.asoc.2011.08.049.

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29

Xia, Bin, Xianzhi Zheng, Liye Zhang, and Lei Zhao. "UWB Positioning System Based on Genetic Algorithm." Journal of Computer and Communications 09, no. 04 (2021): 110–18. http://dx.doi.org/10.4236/jcc.2021.94008.

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30

Alam, M. S. "Dynamic Modelling of Flexible Manipulator System Using Genetic Algorithm." Dhaka University Journal of Science 60, no. 2 (August 3, 2012): 239–45. http://dx.doi.org/10.3329/dujs.v60i2.11526.

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Flexible robotic manipulators pose various challenges in modelling, design, structural optimisation and control. This paper presents investigations into practical dynamic modelling of a flexible manipulator system using genetic algorithm (GA). Conventional genetic algorithms (GAs) often converge prematurely to a suboptimal region and fail to provide effective solutions due to lack of diversity in the population set as the algorithm proceeds. In order to improve and maintain diversity in the population set, a relatively new variant of GA, namely, fitness sharing based replacement genetic algorithm (FSR-GA1) is employed where some individuals are replaced periodically based on a fitness sharing method. The algorithm is utilised to extract dynamic model of 1-DOF (degree of freedom) motion of a flexible manipulator system. A comparative assessment between FSR-GA and conventional GA is presented in the same application to highlight the novelty of the used GA. Results show that the FSR-GA significantly improves the searching capability of the optimisation process compared to conventional GA. Time domain and frequency domain results clearly reveal the potential of the proposed method in modelling flexible manipulator systems.DOI: http://dx.doi.org/10.3329/dujs.v60i2.11526 Dhaka Univ. J. Sci. 60(2): 239-245, 2012 (July)

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31

Harun, Sariffuddin, and Mohd Faisal Ibrahim. "A genetic algorithm based task scheduling system for logistics service robots." Bulletin of Electrical Engineering and Informatics 8, no. 1 (March 1, 2019): 206–13. http://dx.doi.org/10.11591/eei.v8i1.1437.

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The demand for autonomous logistics service robots requires an efficient task scheduling system in order to optimise cost and time for the robot to complete its tasks. This paper presents a Genetic algorithm (GA) based task scheduling system for a ground mobile robot that is able to find a global near-optimal travelling path to complete a logistics task of pick-and-deliver items at various locations. In this study, the chromosome representation and the fitness function of GA is carefully designed to cater for a single load logistics robotic task. Two variants of GA crossover are adopted to enhance the performance of the proposed algorithm. The performance of the scheduling is compared and analysed between the proposed GA algorithms and a conventional greedy algorithm in a virtual map and a real map environments that turns out the proposed GA algorithms outperform the greedy algorithm by 40% to 80% improvement.

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32

Dorigo, Marco. "Genetic and Non-Genetic Operators in ALECSYS." Evolutionary Computation 1, no. 2 (June 1993): 151–64. http://dx.doi.org/10.1162/evco.1993.1.2.151.

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It is well known that standard learning classifier systems, when applied to many different domains, exhibit a number of problems: payoff oscillation, difficulty in regulating interplay between the reward system and the background genetic algorithm (GA), rule chains' instability, default hierarchies' instability, among others. ALECSYS is a parallel version of a standard learning classifier system (CS) and, as such, suffers from these same problems. In this paper we propose some innovative solutions to some of these problems. We introduce the following original features. Mutespec is a new genetic operator used to specialize potentially useful classifiers. Energy is a quantity introduced to measure global convergence to apply the genetic algorithm only when the system is close to a steady state. Dynamic adjustment of the classifiers set cardinality speeds up the performance phase of the algorithm. We present simulation results of experiments run in a simulated two-dimensional world in which a simple agent learns to follow a light source.

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33

Ławrynowicz, Anna. "Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems." Foundations of Management 3, no. 2 (January 1, 2011): 7–26. http://dx.doi.org/10.2478/v10238-012-0039-2.

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Genetic Algorithms for Solving Scheduling Problems in Manufacturing SystemsScheduling manufacturing operations is a complicated decision making process. From the computational point of view, the scheduling problem is one of the most notoriously intractable NP-hard optimization problems. When the manufacturing system is not too large, the traditional methods for solving scheduling problem proposed in the literature are able to obtain the optimal solution within reasonable time. But its implementation would not be easy with conventional information systems. Therefore, many researchers have proposed methods with genetic algorithms to support scheduling in the manufacturing system. The genetic algorithm belongs to the category of artificial intelligence. It is a very effective algorithm to search for optimal or near-optimal solutions for an optimization problem. This paper contains a survey of recent developments in building genetic algorithms for the advanced scheduling. In addition, the author proposes a new approach to the distributed scheduling in industrial clusters which uses a modified genetic algorithm.

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Pedram, Ali. "A Method for Scheduling Multi Processing Systems with Genetic Algorithm." International Journal of Engineering and Technology 1, no. 2 (2009): 179–83. http://dx.doi.org/10.7763/ijet.2009.v1.33.

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35

Darrah, Marjorie, Jay Wilhelm, Thilanka Munasinghe, Kristin Duling, Steve Yokum, Eric Sorton, Jonathan Rojas, and Mitchell Wathen. "A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing." Unmanned Systems 03, no. 01 (January 2015): 49–62. http://dx.doi.org/10.1142/s2301385015500041.

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This paper discusses the development and testing of a flexible genetic algorithm (GA)-based system used for tasking a team of unmanned aerial vehicles (UAVs) to complete a coordinated surveillance mission. The GA development, laboratory testing of the GA to ensure convergence to a "good" solution, integration testing with two ground stations, and the field testing of the algorithms are explained. The algorithm was found to be robust and flexible enough to work in various settings with different UAV types and ground stations.

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36

Odeh, S. M., A. M. Mora, M. N. Moreno, and J. J. Merelo. "A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System." Advances in Fuzzy Systems 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/378156.

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This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.

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Bai, Longju. "Reconfiguration Performance of the Urban Power Distribution System Based on the Genetic-Ant Colony Fusion Algorithm." E3S Web of Conferences 257 (2021): 02062. http://dx.doi.org/10.1051/e3sconf/202125702062.

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This study aims to enhance the reliability of the urban power grid system and decrease the economic loss due to power network faults. Based on the analysis of the traditional algorithms for restructuring the urban distribution system after faults, this study proposes an upgraded genetic algorithm (GA) and ant colony algorithm (ACA) and combines these two to overcome the limitations of the local optimum of GAs and low convergence speed of ACAs. Taking the IEEE33-node system as the research object, the network loss, maximum recovery of the power-loss load, and the number of switching operations as the objective function, the impact of different algorithms on the restoration and reconfiguration of the distribution system was examined according to MATLAB system simulation and the optimal algorithm for the reconfiguration of the urban distribution system failure recovery. The experimental results revealed that compared with the current distribution system reconfiguration algorithm, the genetic-ant colony algorithm (GACA) has higher algorithm time efficiency and solution accuracy and can markedly decrease the recovery time and improve the impact of the distribution system in a short period. Overall, the proposed GACA is an efficient self-healing algorithm of urban distribution systems and useful for augmenting the reliability of the urban power system.

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Gayibov, Tulkin, and Behzod Pulatov. "Taking into account the constraints in power system mode optimization by genetic algorithms." E3S Web of Conferences 264 (2021): 04045. http://dx.doi.org/10.1051/e3sconf/202126404045.

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Over the past decades, many publications on the use of genetic algorithms, which offer a new and powerful approach for solving the problem of power system mode optimization, have appeared. Despite this, the issues of effectively taking into account various constraints when solving such problems with genetic algorithms remain opened. In this regard, this article proposes an algorithm for optimizing power system modes by genetic algorithm, taking into account functional constraints in the form of equalities and inequalities by various penalty functions. The results of effectiveness research of the given algorithm in the example of optimization of 8-nodal power system with four thermal power plants and three lines with controlled power flows are presented.

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39

Bisen, Minakshi, and Amit Dubey. "An Intrusion Detection System based on Support Vector Machine using Hierarchical Clustering and Genetic Algorithm." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 06, no. 01 (February 9, 2018): 08–12. http://dx.doi.org/10.9756/sijcsea/v6i1/03010040101.

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40

T.A, Prasetyo. "Genetic Algorithm in Control System for Dengue Model Analysis with Vaccination, Repellent, and Wolbachia Scheme." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1208–18. http://dx.doi.org/10.5373/jardcs/v12sp7/20202221.

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41

Kim. "An Analysis of the Effects of Walking Guidance System in Subway Stations using Genetic Algorithm." Journal of the Korean Society of Civil Engineers 35, no. 3 (2015): 617. http://dx.doi.org/10.12652/ksce.2015.35.3.0617.

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42

Kikuchi, S., D. Tominaga, M. Arita, K. Takahashi, and M. Tomita. "Dynamic modeling of genetic networks using genetic algorithm and S-system." Bioinformatics 19, no. 5 (March 22, 2003): 643–50. http://dx.doi.org/10.1093/bioinformatics/btg027.

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Lin, Man, and Sai Man Ng. "A Genetic Algorithm for Energy Aware Task Scheduling in Heterogeneous Systems." Parallel Processing Letters 15, no. 04 (December 2005): 439–49. http://dx.doi.org/10.1142/s0129626405002350.

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In distributed systems, an application can be decomposed to tasks which can be executed on different processors in parallel. Modern processors allow variable supply voltages and dynamic voltage scaling (DVS) provides the possibility to reduce the power consumption. In this paper, we present a static scheduling approach to integrate task mapping, scheduling and voltage selection to minimize energy consumption of real-time dependent tasks executing on a number of heterogeneous processors. The approach is based on Genetic Algorithms. The simulation results show that the proposed algorithm is very effective and reduces the energy consumption ranging from 20% to 90% under different system configurations. We also compare the proposed genetic-algorithm-based energy aware algorithm with other three algorithms, namely earliest-deadline-first-based, longest-time-first-based and simulated-annealing-based energy aware algorithms. The comparison results demonstrate that the genetic-algorithm-based energy aware algorithm outperforms other three algorithms.

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Tkachuk, Valerii. "Quantum Genetic Algorithm Based on Qutrits and Its Application." Mathematical Problems in Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/8614073.

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Typical approaches to designing quantum genetic algorithms are based on a concept of a qubit, a two-level quantum system. But many-valued quantum logic is more perspective from the point of view of the computational power. This paper proposes a quantum genetic algorithm based on a three-level quantum system in order to accelerate evolutionary process. Simulation using a set of standard test functions proves that the given algorithm is more effective and precise than the conventional quantum genetic algorithm.

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Hou, Caiping, and Xiyu Liu. "Tissue-like P system based DNA-GA for clustering." TELKOMNIKA Indonesian Journal of Electrical Engineering 16, no. 3 (December 1, 2015): 565. http://dx.doi.org/10.11591/tijee.v16i3.1649.

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In recent years, DNA GA algorithm is drawing attention from scholars. The algorithm combines the DNA encoding and Genetic Algorithm, which solve the premature convergence of genetic algorithms, the weak local search capability and binary Hamming cliff problems effectively.How to design a more effective way to improve the performance of DNA-GA algorithm is more worth studying. As is known to all,the tissue-like P system can search for the optimal clustering partition with the help of its parallel computing advantage effectivel. This paper is under this premise and presents DNA-GA algorithm based on tissue-like P systems (TPDNA-GA) with a loop structure of cells, which aims to combine the parallelism and the evolutionary rules of tissue-like P systems to improve performance of the DNA-GA algorithm. The objective of this paper is to use the TPDNA-GA algorithm to support clustering in order to find the best clustering center.This algorithm is of particular interest to when dealing with large and heterogeneous data sets and when being faced with an unknown number of clusters. Experimental results show that the proposed TPDNA-GA algorithm for clustering is superior or competitive to classical k-means algorithm and several evolutionary clustering algorithms.

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Jeya, S., and S. Muthu Perumal Pillai. "Data stream classification Detection System Using Genetic Algorithm." i-manager's Journal on Software Engineering 6, no. 1 (September 15, 2011): 36–44. http://dx.doi.org/10.26634/jse.6.1.1537.

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47

Yamauchi, Shigeki, and Kenji Tezuka. "Automatic Nesting System by Use of Genetic Algorithm." Journal of the Society of Naval Architects of Japan 1995, no. 178 (1995): 707–12. http://dx.doi.org/10.2534/jjasnaoe1968.1995.178_707.

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48

Ahmed S. Al-Abdulwahab, Ahmed S. Al-Abdulwahab. "Generating System Wellbeing Index Evaluation Using Genetic Algorithm." journal of King Abdulaziz University Engineering Sciences 23, no. 2 (February 4, 2012): 37–53. http://dx.doi.org/10.4197/eng.23-2.3.

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Reliability assessment of generation system is a crucial task used to be done using deterministic approaches. However, due to the practical limitations of these approaches, they have been gradually replaced by probabilistic techniques. Nevertheless, there is a considerable reluctance in many electric power utilities to completely abandon deterministic considerations. To fulfill the industry need, wellbeing analysis has been developed to combine the deterministic and the probabilistic approaches in a single framework. Analytical techniques or Monte Carlo Simulation have been used to evaluate wellbeing indices. However, analytical approaches are complicated and mathematically demanding and simulation technique requires a huge amount of computing time, and large memory size. This still prevents the utilities to benefit from the wellbeing framework. This paper presents a novel Genetic Algorithm (GA) based technique to calculate the wellbeing indices. Hopefully, this will encourage the industry to benefit from the wellbeing analysis. The features of the GA are utilized to collect and identify the health, marginal and at risk wellbeing states and to calculate the associated wellbeing indices. The proposed technique is applied to the IEEE-RBTS and the resulting wellbeing indices are compared to those obtained using a conventional analytical technique. The results show that the outcome of both techniques is virtually identical. The effect of the GA parameters on the wellbeing indices is examined. The proposed GA based technique in the manner applied in this study is simple, practical and valid to calculate the wellbeing indices.

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Parag, Allon, and Daniel R. Lewin. "Decentralized Control System Synthesis Using a Genetic Algorithm." IFAC Proceedings Volumes 29, no. 1 (June 1996): 1175–80. http://dx.doi.org/10.1016/s1474-6670(17)57824-6.

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Cohen, Miri Weiss, Michael Aga, and Tomer Weinberg. "Genetic Algorithm Software System for Analog Circuit Design." Procedia CIRP 36 (2015): 17–22. http://dx.doi.org/10.1016/j.procir.2015.01.033.

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