Skip to main content

Advertisement

Springer Nature Link
Log in

A review on genetic algorithm: past, present, and future

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

See More

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Abbasi M, Rafiee M, Khosravi MR, Jolfaei A, Menon VG, Koushyar JM (2020) An efficient parallel genetic algorithm solution for vehicle routing problem in cloud implementation of the intelligent transportation systems. Journal of cloud Computing 9(6)

  2. Abdelghany A, Abdelghany K, Azadian F (2017) Airline flight schedule planning under competition. Comput Oper Res 87:20–39

    MathSciNet  MATH  Google Scholar 

  3. Abdulal W, Ramachandram S (2011) Reliability-aware genetic scheduling algorithm in grid environment. International Conference on Communication Systems and Network Technologies, Katra, Jammu, pp 673–677

    Google Scholar 

  4. Abdullah J (2010) Multiobjectives ga-based QoS routing protocol for mobile ad hoc network. Int J Grid Distrib Comput 3(4):57–68

    Google Scholar 

  5. Abo-Elnaga Y, Nasr S (2020) Modified evolutionary algorithm and chaotic search for Bilevel programming problems. Symmetry 12:767

    Google Scholar 

  6. Afrouzy ZA, Nasseri SH, Mahdavi I (2016) A genetic algorithm for supply chain configuration with new product development. Comput Ind Eng 101:440–454

    Google Scholar 

  7. Aiello G, Scalia G (2012) La, Enea M. A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding Expert Syst Appl 39(12):10352–10358

    Google Scholar 

  8. Alaoui A, Adamou-Mitiche ABH, Mitiche L (2020) Effective hybrid genetic algorithm for removing salt and pepper noise. IET Image Process 14(2):289–296

    Google Scholar 

  9. Alkhafaji BJ, Salih MA, Nabat ZM, Shnain SA (2020) Segmenting video frame images using genetic algorithms. Periodicals of Engineering and Natural Sciences 8(2):1106–1114

    Google Scholar 

  10. Al-Oqaily AT, Shakah G (2018) Solving non-linear optimization problems using parallel genetic algorithm. International Conference on Computer Science and Information Technology (CSIT), Amman, pp. 103–106

  11. Alvesa MJ, Almeidab M (2007) MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem. Comput Oper Res 34:3458–3470

    MathSciNet  Google Scholar 

  12. Arakaki RK, Usberti FL (2018) Hybrid genetic algorithm for the open capacitated arc routing problem. Comput Oper Res 90:221–231

    MathSciNet  MATH  Google Scholar 

  13. Arkhipov DI, Wu D, Wu T, Regan AC (2020) A parallel genetic algorithm framework for transportation planning and logistics management. IEEE Access 8:106506–106515

    Google Scholar 

  14. Azadeh A, Elahi S, Farahani MH, Nasirian B (2017) A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment. Comput Ind Eng 104:124–133

    Google Scholar 

  15. Baker JE, Grefenstette J (2014) Proceedings of the first international conference on genetic algorithms and their applications. Taylor and Francis, Hoboken, pp 101–105

    Google Scholar 

  16. Bolboca SD, JAntschi L, Balan MC, Diudea MV, Sestras RE (2010) State of art in genetic algorithms for agricultural systems. Not Bot Hort Agrobot Cluj 38(3):51–63

    Google Scholar 

  17. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, Inc

    MATH  Google Scholar 

  18. Burchardt H, Salomon R (2006) Implementation of path planning using genetic algorithms on Mobile robots. IEEE International Conference on Evolutionary Computation, Vancouver, BC, pp 1831–1836

    Google Scholar 

  19. Burdsall B, Giraud-Carrier C (1997) Evolving fuzzy prototypes for efficient data clustering," in second international ICSC symposium on fuzzy logic and applications. Zurich, Switzerland, pp. 217-223.

  20. Burkowski FJ (1999) Shuffle crossover and mutual information. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington, DC, USA, 1999, pp. 1574–1580

  21. Chaiyaratana N, Zalzala AM (2000) "Hybridisation of neural networks and a genetic algorithm for friction compensation," in the 2000 congress on evolutionary computation, vol 1. San Diego, USA, pp 22–29

    Google Scholar 

  22. Chen R, Liang C-Y, Hong W-C, Gu D-X (2015) Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl Soft Comput 26:434–443

    Google Scholar 

  23. J.R. Cheng and M. Gen (2020) Parallel genetic algorithms with GPU computing. Impact on Intelligent Logistics and Manufacturing.

    Google Scholar 

  24. Cheng H, Yang S (2010) Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks. Applications of evolutionary computation. Springer, In, pp 562–571

    Google Scholar 

  25. Cheng H, Yang S, Cao J (2013) Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc net-works. Expert Syst Appl 40(4):1381–1392

    Google Scholar 

  26. Chouhan SS, Kaul A, Singh UP (2018) Soft computing approaches for image segmentation: a survey. Multimed Tools Appl 77(21):28483–28537

    Google Scholar 

  27. Chuang YC, Chen CT, Hwang C (2016) A simple and efficient real-coded genetic algorithm for constrained optimization. Appl Soft Comput 38:87–105

    Google Scholar 

  28. Coello CAC, Pulido GT (2001) A micro-genetic algorithm for multiobjective optimization. In: EMO, volume 1993 of lecture notes in computer science, pp 126–140. Springer

  29. Das, K. N. (2014). Hybrid genetic algorithm: an optimization tool. In global trends in intelligent computing Research and Development (pp. 268-305). IGI global.

  30. Das AK, Pratihar DK (2018) A direction-based exponential mutation operator for real-coded genetic algorithm. IEEE International Conference on Emerging Applications of Information Technology.

    Google Scholar 

  31. Dash SR, Dehuri S, Rayaguru S (2013) Discovering interesting rules from biological data using parallel genetic algorithm, 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad,, pp. 631–636.

  32. Datta D, Amaral ARS, Figueira JR (2011) Single row facility layout problem using a permutation-based genetic algorithm. European J Oper Res 213(2):388–394

    MathSciNet  MATH  Google Scholar 

  33. de Ocampo ALP, Dadios EP (2017) "Energy cost optimization in irrigation system of smart farm by using genetic algorithm," 2017IEEE 9th international conference on humanoid. Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Manila, pp 1–7

    Google Scholar 

  34. Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Systems 9:115–148

    MathSciNet  MATH  Google Scholar 

  35. Deb K, Deb D (2014) Analysing mutation schemes for real-parameter genetic algorithms. International Journal of Artificial Intelligence and Soft Computing 4(1):1–28

    Google Scholar 

  36. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Google Scholar 

  37. Deep K, Das KN (2008) Quadratic approximation based hybrid genetic algorithm for function optimization. Appl Math Comput 203(1):86–98

    MATH  Google Scholar 

  38. Deep K, Thakur M (2007) A new mutation operator for real coded genetic algorithms. Appl Math Comput 193:211–230

    MathSciNet  MATH  Google Scholar 

  39. Deep K, Thakur M (2007) A new crossover operator for real coded genetic algorithms. Appl Math Comput 188:895–911

    MathSciNet  MATH  Google Scholar 

  40. Dhal KP, Ray S, Das A, Das S (2018) A survey on nature-inspired optimization algorithms and their application in image enhancement domain. Archives of Computational Methods in Engineering 5:1607–1638

    Google Scholar 

  41. Dhiman G, Kumar V (2017) Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70

    Google Scholar 

  42. Dhiman G, Kumar V (2018) Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowl-Based Syst 159:20–50

    Google Scholar 

  43. Dhiman G, Kumar V (2019) Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl-Based Syst 165:169–196

    Google Scholar 

  44. Di Fatta G, Hoffmann F, Lo Re G, Urso A (2003) A genetic algorithm for the design of a fuzzy controller for active queue management. IEEE Trans Syst Man Cybern Part C Appl Rev 33(3):313–324

    Google Scholar 

  45. Diabat A, Deskoores R (2016) A hybrid genetic algorithm based heuristic for an integrated supply chain problem. J Manuf Syst 38:172–180

    Google Scholar 

  46. Diaz-Manríquez A, Ríos-Alvarado AB, Barrón-Zambrano JH, Guerrero-Melendez TY, Elizondo-Leal JC (2018) An automatic document classifier system based on genetic algorithm and taxonomy. IEEE Access 6:21552–21559. https://doi.org/10.1109/ACCESS.2018.2815992

    Article  Google Scholar 

  47. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization - artificial ants as a computational intelligence technique. IEEE Comput Intell Mag 1(2006):28–39

    Google Scholar 

  48. Ebrahimzadeh R, Jampour M (2013) Chaotic genetic algorithm based on Lorenz chaotic system for optimization problems. I.J. Intelligent Systems and Applications Intelligent Systems and Applications 05(05):19–24

    Google Scholar 

  49. EkbataniFard GH, Monsefi R, Akbarzadeh-T M-R, Yaghmaee M et al. (2010) A multi-objective genetic algorithm based approach for energy efficient qos-routing in two-tiered wireless sensor net-works. In: wireless pervasive computing (ISWPC), 2010 5th IEEE international symposium on. IEEE, pp 80–85

  50. El-Mihoub T, Hopgood A, Nolle L, Battersby A (2004) Performance of hybrid genetic algorithms incorporating local search. In: Horton G (ed) 18th European simulation multi-conference (ESM2004). Germany, Magdeburg, pp 154–160

    Google Scholar 

  51. El-Mihoub TA, Hopgood AA, Lars N, Battersby A (2006) Hybrid genetic algorithms: A review. Eng Lett 13:2

    Google Scholar 

  52. Emmerich MTM, Deutz AH (2018) A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Nat Comput 17(3):585–609

    MathSciNet  Google Scholar 

  53. Eshelman LJ, Caruana RA, Schaffer JD (1997) Biases in the crossover landscape.

  54. Espinoza FB, Minsker B, Goldberg D (2003) Performance evaluation and population size reduction for self adaptive hybrid genetic algorithm (SAHGA), in the Genetic and Evolutionary Computation Conference, vol. 2723, Lecture Notes in Computer Science San Francisco, USA: Springer, pp. 922–933.

  55. Farahani RZ, Elahipanah M (2008) A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. Int J Prod Econ 111(2):229–243

    Google Scholar 

  56. Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: ICGA, pp 416–423. Morgan Kaufmann

  57. Fox B, McMahon M (1991) Genetic operators for sequencing problems, in Foundations of Genetic Algorithms, G. Rawlins, Ed. Morgan Kaufmann Publishers, San Mateo,CA, Ed. 1991, pp. 284–300.

  58. Freisleben B, Merz P (1996) New genetic local search operators for the traveling salesman problem," in the Fourth Conference on Parallel Problem Solving from Nature vol. 1141, Lectures Notes in Computer Science, H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, Eds. Berlin, Germany: Springer-Verlag, pp. 890–899.

  59. Friend DH, EI Nainay, M, Shi Y, MacKenzie AB (2008) Architecture and performance of an island genetic algorithm-based cognitive network. In: Consumer communications and networking conference,2008. CCNC 2008. 5th IEEE. IEEE, pp 993–997

  60. Fuertes G, Vargas M, Alfaro M, Soto-Garrido R, Sabattin J, Peralta M-A (2019) Chaotic genetic algorithm and the effects of entropy in performance optimization.

  61. Ghaheri A, Shoar S, Naderan M, Hoseini SS (2015) The applications of genetic algorithms in medicine. CJ 30:406–416

    Google Scholar 

  62. Ghosh S, Bhattachrya S (2020) A data-driven understanding of COVID-19 dynamics using sequential genetic algorithm based probabilistic cellular automata. Applied Soft Computing. 96

  63. Ghoshal AK, Das N, Bhattacharjee S, Chakraborty G (2019) A fast parallel genetic algorithm based approach for community detection in large networks. International Conference on Communication Systems & Networks (COMSNETS), Bengaluru, India, pp. 95–101.

  64. Gogna A, Tayal A (2012) Comparative analysis of evolutionary algorithms for image enhancement. Int J Met 2(1)

  65. Goldberg D (1989) Genetic algorithm in search. Optimization and Machine Learning, Addison -Wesley, Reading, MA 1989

  66. Goldberg D, Lingle R (1985) Alleles, loci and the traveling salesman problem. In: Proceedings of the 1st international conference on genetic algorithms and their applications, vol. 1985. Los Angeles, USA, pp 154–159

    Google Scholar 

  67. Guido R, Conforti D (2017) A hybrid genetic approach for solving an integrated multi-objective operating room planning and scheduling problem. Comput Oper Res 87:270–282

    MathSciNet  MATH  Google Scholar 

  68. Ha QM, Deville Y, Pham QD, Ha MH (2020) A hybrid genetic algorithm for the traveling salesman problem with drone. J Heuristics 26:219–247

    Google Scholar 

  69. HajiRassouliha A, Gamage TPB, Parker MD, Nash MP, Taberner AJ, Nielsen, PM (2013) FPGA implementation of 2D cross-correlation for real-time 3D tracking of deformable surfaces. In Proceedings of the2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013), Wellington, New Zealand, 27–29 November 2013; IEEE: Piscataway, NJ, USA; pp. 352–357

  70. Harada T, Alba E (2020) Parallel genetic algorithms: a useful survey. ACM Computing Survey 53(4):1–39

    Google Scholar 

  71. Harik GR, Lobo FG (1999) A parameter-less genetic algorithm, in Proceedings of the Genetic and Evolutionary Computation Conference, pp. 258–265.

  72. Hassanat A, Almohammadi K, Alkafaween E, Abunawas E, Hammouri A, Prasath VBS (December 2019) Choosing mutation and crossover ratios for genetic algorithms—A review with a new dynamic approach. Information 10:390. https://doi.org/10.3390/info10120390

    Article  Google Scholar 

  73. He J, Ji S, Yan M, Pan Y, Li Y (2012) Load-balanced CDS construction in wireless sensor networks via genetic algorithm. Int J Sens Netw 11(3):166–178

    Google Scholar 

  74. Hedar A, Fukushima M (2003) Simplex coding genetic algorithm for the global optimization of nonlinear functions, in Multi-Objective Programming and Goal Programming, Advances in Soft Computing, T. Tanino, T. Tanaka, and M. Inuiguchi, Eds.: Springer-Verlag, pp. 135–140.

  75. Helal MHS, Fan C, Liu D, Yuan S (2017) Peer-to-peer based parallel genetic algorithm. International Conference on Information, Communication and Engineering (ICICE), Xiamen, pp 535–538

    Google Scholar 

  76. Hiassat A, Diabat A, Rahwan I (2017) A genetic algorithm approach for location-inventory-routing problem with perishable products. J Manuf Syst 42:93–103

    Google Scholar 

  77. Holland JH (1975) Adaptation in natural and artificial systems. The U. of Michigan Press

  78. Hong W-C, Dong Y, Chen L-Y, Wei S-Y (2011) SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Appl Soft Comput 11(2):1881–1890

    Google Scholar 

  79. Hong T-P, Lee Y-C, Min-Thai W (2014) An effective parallel approach for genetic-fuzzy data mining. Exp Syst Applic 41(2):655–662

    Google Scholar 

  80. Horn J, Nafpliotis N, Goldberg DE. (1994) A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, vol. 1, Piscataway, NJ: IEEE Service Center, p. 67–72.

  81. Hu C, Wang X, Mandal MK, Meng M, Li D (2003) Efficient face and gesture recognition techniques for robot control. Department of Electrical and Computer Engineering University of Alberta, Edmonton, AB, T6G 2V4, Canada. CCECE2003 - CCGEI 2003, Montreal, May/mai 2003 IEEE, pp 1757-1762.

  82. Peng Huo, Simon C. K. Shiu, Haibo Wang, Ben Niu (2009) Application and Comparison of Particle Swarm Optimization and Genetic Algorithm in Strategy Defense Game. Fifth International Conference on Natural Computation, pp 387–392.

  83. Hussain A, Muhammad YS, Nauman Sajid M, Hussain I, Mohamd Shoukry A, Gani S (2017) Genetic algorithm for traveling salesman problem with modified cycle crossover operator. Computational intelligence and neuroscience 2017:1–7

    Google Scholar 

  84. Ishibuchi H, Murata T (1998) A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans Syst Man Cybern Part C Appl Rev 28(3):392–403

    Google Scholar 

  85. Jafari A, Khalili T, Babaei E, Bidram A (2020) Hybrid optimization technique using exchange market and GA. IEEE Access 8:2417–2427

    Google Scholar 

  86. Jaszkiewicz A (February 2002) Genetic local search for multi-objective combinatorial optimization. Eur J Oper Res 137(1):50–71

    MathSciNet  MATH  Google Scholar 

  87. Javidi M, Hosseinpourfard R (2015) Chaos genetic algorithm instead genetic algorithm. Int J Inf Tech 12(2):163–168

    Google Scholar 

  88. Jebari K (2013) Selection methods for genetic algorithms. Abdelmalek Essaâdi University. International Journal of Emerging Sciences 3(4):333–344

    Google Scholar 

  89. Jiang S, Chin K-S, Wang L, Qu G, Tsui KL (2017) Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department. Expert Syst Appl 82:216–230

    Google Scholar 

  90. Jiang M, Fan X, Pei Z, Zhang Z (2018) Research on text feature clustering based on improved parallel genetic algorithm. Tenth International Conference on Advanced Computational Intelligence (ICACI), Xiamen, pp. 235–238

  91. Kaluri R, Reddy P (2016) Sign gesture recognition using modified region growing algorithm and adaptive genetic fuzzy classifier. International Journal of Intelligent Engineering and Systems 9(4):225–233

    Google Scholar 

  92. Kandavanam G, Botvich D, Balasubramaniam S, Jennings B (2010) A hybrid genetic algorithm/variable neighborhood search approach to maximizing residual bandwidth of links for route planning. Artificial evolution. Springer, In, pp 49–60

    Google Scholar 

  93. Kannan S (2020) Intelligent object recognition in underwater images using evolutionary-based Gaussian mixture model and shape matching. SIViP 14:877–885

    Google Scholar 

  94. Karabudak D, Hung C-C, Bing B (2004) A call admission control scheme using genetic algorithms. In: Proceedings of the 2004ACM symposium on applied computing. ACM, pp 1151–1158

  95. Katz P, Aron M, Alfalou A (2001) A face-tracking system to detect falls in the elderly; SPIE newsroom. SPIE, Bellingham, WA, USA, p 201

    Google Scholar 

  96. Kaur M, Kumar V (2018) Beta chaotic map based image encryption using genetic algorithm. Int J Bifurcation Chaos 28(11):1850132

    MathSciNet  MATH  Google Scholar 

  97. Kaur M, Kumar V (2018) Parallel non-dominated sorting genetic algorithm-II-based image encryption technique. The Imaging Science Journal. 66(8):453–462

    Google Scholar 

  98. Kaur M, Kumar V (2018) Fourier–Mellin moment-based intertwining map for image encryption. Modern Physics Letters B 32(9):1850115

    MathSciNet  Google Scholar 

  99. Kaur G, Bhardwaj N, Singh PK (2018) An analytic review on image enhancement techniques based on soft computing approach. Sensors and Image Processing, Advances in Intelligent Systems and Computing 651:255–266

    Google Scholar 

  100. Kavitha AR, Chellamuthu C (2016) Brain tumour segmentation from MRI image using genetic algorithm with fuzzy initialisation and seeded modified region growing (GFSMRG) method. The Imaging Science Journal 64(5):285–297

    Google Scholar 

  101. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks (1995), pp 1942–1948

    Google Scholar 

  102. Khan, A., ur Rehman, Z., Jaffar, M.A., Ullah, J., Din, A., Ali, A., Ullah, N. (2019) Color image segmentation using genetic algorithm with aggregation-based clustering validity index (CVI). SIViP 13(5), 833–841

  103. Kia R, Khaksar-Haghani F, Javadian N, Tavakkoli-Moghaddam R (2014) Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm. J Manuf Syst 33(1):218–232

    Google Scholar 

  104. Kim EY, Jung K (2006) Genetic algorithms for video segmentation. Pattern Recogn 38(1):59–73

    MathSciNet  Google Scholar 

  105. Kim EY, Park SH (2006) Automatic video segmentation using genetic algorithms. Pattern Recogn Lett 27(11):1252–1265

    Google Scholar 

  106. Kita H, Ono I, Kobayashi S (1999). The multi-parent unimodal normal distribution crossover for real-coded genetic algorithms. Proceedings of the 1999 Congress on Evolutionary Computation, vol. 2, IEEE (1999), pp. 1588–1595

  107. Kobayashi H, Munetomo M, Akama K, Sato Y (2004) Designing a distributed algorithm for bandwidth allocation with a genetic algorithm. Syst Comput Jpn 35(3):37–45

    Google Scholar 

  108. Konak A, Smith AE (1999) A hybrid genetic algorithm approach for backbone design of communication networks, in the 1999 Congress on Evolutionary Computation. Washington D.C, USA: IEEE, pp. 1817-1823.

  109. Kortil Y, Jridi M, Falou AA, Atri M (2020) Face recognition systems: A survey. Sensors. 20:1–34

    Google Scholar 

  110. Krishnan N, Muthukumar S, Ravi S, Shashikala D, Pasupathi P (2013) Image restoration by using evolutionary technique to Denoise Gaussian and impulse noise. In: Prasath R., Kathirvalavakumar T. (eds) mining intelligence and knowledge exploration. Lecture notes in computer science, vol 8284. Springer, Cham.

  111. Kumar A (2013) Encoding schemes in genetic algorithm. Int J Adv Res IT Eng 2(3):1–7

    Google Scholar 

  112. Kumar V, Kumar D (2017) An astrophysics-inspired grey wolf algorithm for numerical optimization and its application to engineering design problems. Adv Eng Softw 112:231–254

    Google Scholar 

  113. Kumar V, Chhabra JK, Kumar D (2014) Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems. J Comput Sci 5(2):144–155

    MathSciNet  Google Scholar 

  114. Kumar C, Singh AK, Kumar P (2017) A recent survey on image watermarking techniques and its application in e-governance. MultiMed Tools Appl.

    Google Scholar 

  115. Kurdi M (2016) An effective new island model genetic algorithm for job shop scheduling problem. Comput Oper Res 67(2016):132–142

    MathSciNet  MATH  Google Scholar 

  116. Larranaga P, Kuijpers CMH, Murga RH, Yurramendi Y (July 1996) Learning Bayesian network structures by searching for the best ordering with genetic algorithms. in IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 26(4):487–493

    Google Scholar 

  117. Larranaga P, Kuijpers C, Murga R, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artificial Intelligence Review 13:129–170

    Google Scholar 

  118. Chang-Yong Lee (2003) Entropy-Boltzmann selection in the genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 33, no. 1, pp. 138–149, Feb. 2003.

  119. Lee CKH (2018) A review of applications of genetic algorithms in operations management. Eng Appl Artif Intell 76:1–12

    Google Scholar 

  120. Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T (July 2001) Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. in IEEE Transactions on Medical Imaging 20(7):595–604

    Google Scholar 

  121. Joon-Yong Lee, Min-Soeng Kim, Cheol-Taek Kim and Ju-Jang Lee (2007) Study on encoding schemes in compact genetic algorithm for the continuous numerical problems,SICE Annual Conference 2007, Takamatsu, pp. 2694–2699.

  122. Leng LT (1999) Guided genetic algorithm. University of Essex, Doctoral Dissertation

    Google Scholar 

  123. Li B, Li J, Tang K, Yao X (2015) Many-objective evolutionary algorithms: A survey. ACM Computing surveys

    Google Scholar 

  124. Lie Tang L (2000) Tian and Brian L steward, "color image segmentation with genetic algorithm for in-field weed sensing". Transactions of the ASAE 43(4):1019–1027

    Google Scholar 

  125. Lima S.J.A., de Araújo S.A. (2018) A new binary encoding scheme in genetic algorithm for solving the capacitated vehicle routing problem. In: Korošec P., Melab N., Talbi EG. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2018. Lecture notes in computer science, vol 10835. Springer, Cham

  126. Liu D (2019) Mathematical modeling analysis of genetic algorithms under schema theorem. Journal of Computational Methods in Sciences and Engineering 19:S131–S137

    Google Scholar 

  127. Liu Z, Meng Q, Wang S (2013) Speed-based toll design for cordon-based congestion pricing scheme. Transport Res Part C: Emerg Technol 31(2013):83–98

    Google Scholar 

  128. Lorenzo B, Glisic S (2013) Optimal routing and traffic scheduling for multihop cellular networks using genetic algorithm. IEEE Trans Mob Comput 12(11):2274–2288

    Google Scholar 

  129. Lucasius CB, Kateman G (1989) Applications of genetic algorithms in chemometrics. In: Proceedings of the 3rd international conference on genetic algorithms. Morgan Kaufmann, Los Altos, CA, USA, pp 170–176

    Google Scholar 

  130. Luo B, Jinhua Zheng, Jiongliang Xie, Jun Wu. Dynamic crowding distance – a new diversity maintenance strategy for MOEAs. ICNC ‘08, Fourth Int. Conf. on Natural Comp., vol. 1 (2008), pp. 580–585

  131. Maghawry A, Kholief M, Omar Y, Hodhod R (2020) An approach for evolving transformation sequences using hybrid genetic algorithms. Int J Intell Syst 13(1):223–233

    Google Scholar 

  132. Manzoni L, Mariot L, Tuba E (2020) Balanced crossover operators in genetic algorithms. Swarm and Evolutionary Computation 54:100646

    Google Scholar 

  133. Mazinani M, Abedzadeh M, Mohebali N (2013) Dynamic facility layout problem based on flexible bay structure and solving by genetic algorithm. Int J Adv Manuf Technol 65(5–8):929–943

    Google Scholar 

  134. Mehboob U, Qadir J, Ali S, Vasilakos A (2016) Genetic algorithms in wireless networking: techniques, applications, and issues. Soft Comput 20:2467–2501

    Google Scholar 

  135. Michalewicz Z (1992) Genetic algorithms + data structures = evolution programs. Springer-Verlag, New York

    MATH  Google Scholar 

  136. Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4(1):1–32

    Google Scholar 

  137. Mishra R, Das KN (2017). A novel hybrid genetic algorithm for unconstrained and constrained function optimization. In bio-inspired computing for information retrieval applications (pp. 230-268). IGI global

  138. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1000097

    Google Scholar 

  139. Mooi S, Lim S, Sultan M, Bakar A, Sulaiman M, Mustapha A, Leong KY (2017) Crossover and mutation operators of genetic algorithms. International Journal of Machine Learning and Computing 7:9–12

    Google Scholar 

  140. Mudaliar DN, Modi NK (2013) Unraveling travelling salesman problem by genetic algorithm using m-crossover operator. International Conference on Signal Processing, Image Processing & Pattern Recognition, Coimbatore, pp 127–130

    Google Scholar 

  141. T. Murata and M. Gen (2000) Cellular genetic algorithm for multi-objective optimization, in Proceedings of the Fourth Asian Fuzzy System Symposium, pp. 538–542

  142. Neto JC, Meyer GE, Jones DD (2006) Individual leaf extractions from young canopy images using gustafsonkessel clustering and a genetic algorithm. Comput Electron Agric 51(1):66–85

    Google Scholar 

  143. NKFC, Viswanatha SDK (2009) Routing algorithm using mobile agents and genetic algorithm. Int J Comput Electr Eng, vol 1, no 3

  144. Ono I, Kobayashi S (1997) A real-coded genetic algorithm for functional optimization using unimodal normal distribution crossover. In: Back T (ed) Proceedings of the 7th international conference on genetic algorithms, ICGA-7. Morgan Kaufmann, East Lansing, MI, USA, pp 246–253

    Google Scholar 

  145. Pachepsky Y, Acock B (1998) Stochastic imaging of soil parameters to assess variability and uncertainty of crop yield estimates. Geoderma 85(2):213–229

    Google Scholar 

  146. Paiva JPD, Toledo CFM, Pedrini H (2016) An approach based on hybrid genetic algorithm applied to image denoising problem. Appl Soft Comput 46:778–791

    Google Scholar 

  147. Palencia AER, Delgadillo GEM (2012) A computer application for a bus body assembly line using genetic algorithms. Int J Prod Econ 140(1):431–438

    Google Scholar 

  148. Palomo-Romero JM, Salas-Morera L, García-Hernández L (2017) An island model genetic algorithm for unequal area facility layout problems. Expert Syst Appl 68:151–162

    Google Scholar 

  149. Pandian S, Modrák V (December 2009) "possibilities, obstacles and challenges of genetic algorithm in manufacturing cell formation," advanced logistic systems, University of Miskolc. Department of Material Handling and Logistics 3(1):63–70

    Google Scholar 

  150. Park Y-B, Yoo J-S, Park H-S (2016) A genetic algorithm for the vendor-managed inventory routing problem with lost sales. Expert Syst Appl 53:149–159

    Google Scholar 

  151. Patel R, Raghuwanshi MM, Malik LG (2012) Decomposition based multi-objective genetic algorithm (DMOGA) with opposition based learning

  152. Pattanaik JK, Basu M, Dash DP (2018) Improved real coded genetic algorithm for dynamic economic dispatch. Journal of electrical systems and information technology. Vol. 5(3):349–362

    Google Scholar 

  153. Payne AW, Glen RC (1993) Molecular recognition using a binary genetic system. J Mol Graph 11(2):74–91

    Google Scholar 

  154. Peerlinck A, Sheppard J, Pastorino J, Maxwell B (2019) Optimal Design of Experiments for precision agriculture using a genetic algorithm. IEEE Congress on Evolutionary Computation.

    Google Scholar 

  155. Pelikan M, Goldberg DE, Cantu-Paz E (2000) Bayesian optimization algorithm, population sizing, and time to convergence, Illinois Genetic Algorithms Laboratory, University of Illinois, Tech. Rep

  156. Pilat ML, White T (2002) Using genetic algorithms to optimize ACS-TSP, in the Third International Workshop on Ant Algorithms, vol. Lecture Notes In Computer Science 2463. Berlin, Germany: Springer-Verlag, pp. 282–287.

  157. Pinagapany S, Kulkarni A (2008) Solving channel allocation problem in cellular radio networks using genetic algorithm. In: Communication Systems software and middleware and workshops, 2008.COMSWARE 2008. 3rd International Conference on. IEEE, pp239–244

  158. Pinel F, Dorronsoro B, Bouvry P (2013) Solving very large instances of the scheduling of independent tasks problem on the GPU. J Parallel Distrib. Comput 73(1):101–110

    Google Scholar 

  159. Pinto G, Ainbinder I, Rabinowitz G (2009) A genetic algorithm-based approach for solving the resource-sharing and scheduling problem. Comput Ind Eng 57(3):1131–1143

    Google Scholar 

  160. Piszcz A, Soule T (2006) Genetic programming: optimal population sizes for varying complexity problems, in Proceedings of the Genetic and Evolutionary Computation Conference, pp. 953–954.

  161. Porta J, Parapar R, Doallo F, Rivera F, Santé I, Crecente R (2013) High performance genetic algorithm for land use planning. Comput Environ Urb Syst 37(2013):45–58

    Google Scholar 

  162. Rafsanjani MK, Riyahi M (2020) A new hybrid genetic algorithm for job shop scheduling problem. International Journal of Advanced Intelligence Paradigms 16(2):157–171

    Google Scholar 

  163. Rathi R, Acharjya DP (2018) A framework for prediction using rough set and real coded genetic algorithm. Arab J Sci Eng 43(8):4215–4227

    Google Scholar 

  164. Rathi R, Acharjya DP (2018) A rule based classification for vegetable production using rough set and genetic algorithm. International Journal of Fuzzy System Applications (IJFSA) 7(1):74–100

    Google Scholar 

  165. Rathi R, Acharjya DP (2020) A comparative study of genetic algorithm and neural network computing techniques over feature selection, In advances in distributed computing and machine learning (pp. 491–500). Springer, Singapore

    Google Scholar 

  166. Ray SS, Bandyopadhyay S, Pal SK (2004) New operators of genetic algorithms for traveling salesman problem," Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., Cambridge pp 497-500

  167. Richter JN, Peak D (2002) Fuzzy evolutionary cellular automata, in international conference on artificial neural networks in engineering, vol 12. USA, Saint Louis pp. 185-191

    Google Scholar 

  168. Riedl A (2002) A hybrid genetic algorithm for routing optimization in ip networks utilizing bandwidth and delay metrics. In: IP operations and management, 2002 IEEE Workshop on. IEEE, pp 166–170

  169. Ripon KSN, Siddique N, Torresen J (2011) Improved precedence preservation crossover for multi-objective job shop scheduling problem. Evolving Systems 2:119–129

    Google Scholar 

  170. Roberge V, Tarbouchi M, Okou F (2014) Strategies to accelerate harmonic minimization in multilevel inverters using a parallel genetic algorithm on graphical processing unit. IEEE Trans Power Electron 29(10):5087–5090

    Google Scholar 

  171. Ronald S (1997) Robust encoding in genetic algorithms: a survey of encoding issues. IEEE international conference on evolutionary computation, pp. 43-48

  172. Roy A, Banerjee N, Das SK (2002) An efficient multi-objective qos-routing algorithm for wireless multicasting. In:Vehicular technology conference, 2002. VTC Spring 2002. IEEE 55th, vol 3., pp 1160–1164

  173. Sadrzadeh A (2012) A genetic algorithm with the heuristic procedure to solve the multi-line layout problem. Comput Ind Eng 62(4):1055–1064

    Google Scholar 

  174. Sahingoz OK (2014) Generation of Bezier curve-based flyable trajectories for multi-UAV systems with parallel genetic algorithm. J Intell Robot Syst 74(1):499–511

    Google Scholar 

  175. Saini N (2017) Review of selection methods in genetic algorithms. International Journal of Engineering and Computer Science 6(12):22261–22263

    Google Scholar 

  176. Sari M, Can T (2018) Prediction of pathological subjects using genetic algorithms. Computational and Mathematical Methods in Medicine 2018:1–9

    Google Scholar 

  177. Scully T, Brown KN (2009) Wireless LAN load balancing with genetic algorithms. Knowl Based Syst 22(7):529–534

    Google Scholar 

  178. Sermpinis G, Stasinakis C, Theofilatos K, Karathanasopoulos A (2015) Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms–support vector regression forecast combinations. European J. Oper. Res. 247(3):831–846

    MathSciNet  MATH  Google Scholar 

  179. Shabankareh SG, Shabankareh SG (2019) Improvement of edge-tracking methods using genetic algorithm and neural network, 2019 5th Iranian conference on signal processing and intelligent systems (ICSPIS). Shahrood, Iran, pp 1–7. https://doi.org/10.1109/ICSPIS48872.2019.9066026

    Book  Google Scholar 

  180. Sharma S, Gupta K (2011) Solving the traveling salesman problem through genetic algorithm with new variation order crossover. International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), Udaipur, pp. 274–276

  181. Sharma N, Kaushik I, Rathi, R, Kumar S (2020) Evaluation of accidental death records using hybrid genetic algorithm. Available at SSRN: https://ssrn.com/abstract=3563084 or https://doi.org/10.2139/ssrn.3563084

  182. Shayeghi A, Gotz D, Davis JBA, Schafer R, Johnston RL (2015) Pool-BCGA: A parallelised generation-free genetic algorithm for the ab initio global optimisation of nano alloy clusters. Phys Chem Chem Phys 17(3):2104–2112

    Google Scholar 

  183. Guoyong Shi, H. Iima and N. Sannomiya (1996) A new encoding scheme for solving job shop problems by genetic algorithm, Proceedings of 35th IEEE Conference on Decision and Control, Kobe, Japan, 1996, pp. 4395–4400 vol.4.

  184. Shi J, Liu Z, Tang L, Xiong J (2017) Multi-objective optimization for a closed-loop network design problem using an improved genetic algorithm. Appl Math Model 45:14–30

    MathSciNet  MATH  Google Scholar 

  185. Shukla AK, Singh P, Vardhan M (2019) A new hybrid feature subset selection framework based on binary genetic algorithm and information theory. International Journal of Computational Intelligence and Applications 18(3):1950020(1–10)

    Google Scholar 

  186. Singh A, Deep K (2015) Real coded genetic algorithm operators embedded in gravitational search algorithm for continuous optimization. Int J Intell Syst Appl 7(12):1

    Google Scholar 

  187. Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithm, 1st edn. Springer-Verlag, Berlin Heidelberg

    MATH  Google Scholar 

  188. Soleimani H, Kannan G (2015) A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Appl Math Model 39(14):3990–4012

    MathSciNet  MATH  Google Scholar 

  189. Soleimani H, Govindan K, Saghafi H, Jafari H (2017) Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Comput Ind Eng 109:191–203

    Google Scholar 

  190. Soon GK, Guan TT, On CK, Alfred R, Anthony P (2013) "A comparison on the performance of crossover techniques in video game," 2013 IEEE international conference on control system. Computing and Engineering, Mindeb, pp 493–498

    Google Scholar 

  191. Srinivas N, Deb K (1995) Multi-objective function optimization using non-dominated sorting genetic algorithms. Evol Comput 2(3):221–248

    Google Scholar 

  192. Subbaraj P, Rengaraj R, Salivahanan S (2011) Enhancement of self-adaptive real-coded genetic algorithm using Taguchi method for economic dispatch problem. Appl Soft Comput 11(1):83–92

    Google Scholar 

  193. Tahir M, Tubaishat A, Al-Obeidat F, et al. (2020) A novel binary chaotic genetic algorithm for feature selection and its utility in affective computing and healthcare. Neural Comput & Appl

  194. Tam V, Cheng K-Y, Lui K-S (2006) Using micro-genetic algorithms to improve localization in wireless sensor networks. J Commun 1(4):1–10

    Google Scholar 

  195. Tan KC, Li Y, Murray-Smith DJ, Sharman KC (1995) System identification and linearisation using genetic algorithms with simulated annealing, in First IEE/IEEE Int. Conf. on GA in Eng. Syst.: Innovations and Appl. Sheffield, UK, pp. 164–169.

  196. Tang PH, Tseng MH (2013) Adaptive directed mutation for real-coded genetic algorithms. Appl Soft Comput 13(1):600–614

    Google Scholar 

  197. Tiong SK, Yap DFW, Koh SP (2012) A comparative analysis of various chaotic genetic algorithms for multimodal function optimization. Trends in Applied Sciences Research 7:785–791

    Google Scholar 

  198. Toutouh J, Alba E (2017) Parallel multi-objective metaheuristics for smart communications in vehicular networks. Soft Comput 21(8):1949–1961

    Google Scholar 

  199. Umbarkar A, Sheth P (2015) Crossover operators in genetic algorithms: a review. Journal on Soft Computing 6(1)

  200. Verma D, Vishwakarma VP, Dalal S (2020) A hybrid self-constrained genetic algorithm (HSGA) for digital image Denoising based on PSNR improvement. Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals, In, pp 135–153

    Google Scholar 

  201. Vitayasak S, Pongcharoen P, Hicks C (2016) A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a genetic algorithm or modified backtracking search algorithm. Int J Prod Econ

  202. Junru Wang and Lan Huang (2014) Evolving gomoku Solver by Genetic Algorithm. IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA) pp 1064–1067.

  203. Wang L, Kan MS, Shahriar Md R, Tan ACC (2014) Different approaches of applying single-objective binary genetic algorithm on the wind farm design. In World Congress on Engineering Asset Management.

  204. Wang N, Li Q, Abd El-Latif AA, Zhang T, Niu X (2014) Toward accurate localization and high recognition performance for noisy iris images. Multimed Tools Appl 71(3):1411–1430

    Google Scholar 

  205. Wang JQ, Ersoy OK, He MY et al (2016) Multi-offspring genetic algorithm and its application to the traveling salesman problem. Appl Soft Comput 43:415–423

    Google Scholar 

  206. Wang FL, Fu XM, Zhu HX et al (2016) Multi-child genetic algorithm based on two-point crossover. J Northeast Agric Univ 47(3):72–79

    Google Scholar 

  207. Wang JQ, Cheng ZW, Ersoy OK et al (2018) Improvement analysis and application of real-coded genetic algorithm for solving constrained optimization problems. Math Probl Eng 2018:1–16

    MathSciNet  MATH  Google Scholar 

  208. Wang J, Zhang M, Ersoy OK, Sun K, Bi Y (2019) An improved real-coded genetic algorithm using the Heuristical Normal distribution and direction-based crossover. Computational Intelligence and Neuroscience 2019:1–17

    Google Scholar 

  209. Wen Z, Yang R, Garraghan P, Lin T, Xu J, Rovatsos M (2017) Fog orchestration for internet of things services. IEEE Internet Comput 21(2) (Mar. 2017):16–24

    Google Scholar 

  210. Wright AH (1991) Genetic algorithms for real parameter optimization. In Foundations of genetic algorithms I,G. J. E. Rawlins, Ed., Morgan Kaufmann, San Mateo, CA,USA

  211. Wu X, Chu C-H, Wang Y, Yan W (2007) A genetic algorithm for cellular manufacturing design and layout. European J Oper Res 181(1):156–167

    MATH  Google Scholar 

  212. Yang S, Cheng H, Wang F (2010) Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. IEEE Trans Syst Man Cybern Part C Appl Rev 40(1):52–63

    Google Scholar 

  213. Yang C, Li H, Rezgui Y, Petri I, Yuce B, Chen B, Jayan B (2014) High throughput computing based distributed genetic algorithm for building energy consumption optimization. Energy Build 76(2014):92–101

    Google Scholar 

  214. Yu F, Xu X (2014) A short-term load forecasting model of natural gas based on optimized genetic algorithm and improve BR neural network. Appl Energy 134:102–113

    Google Scholar 

  215. Yuce B, Fruggiero F, Packianather MS, Pham DT, Mastrocinque E, Lambiase A, Fera M (2017) Hybrid genetic bees algorithm applied to single machine scheduling with earliness and tardiness penalties. Comput Ind Eng 113:842–858

    Google Scholar 

  216. Yun S, Lee J, Chung W, Kim E, Kim S (2009) A soft computing approach to localization in wireless sensor networks. Expert Syst Appl 36(4):7552–7561

    Google Scholar 

  217. Zhai R (2020) Solving the optimization of physical distribution routing problem with hybrid genetic algorithm. J Phys Conf Ser 1550:1–6

    Google Scholar 

  218. Zhang Q, Wang J, Jin C, Zeng Q (2008) Localization algorithm for wireless sensor network based on genetic simulated annealing algorithm. In: 4th IEEE International Conference on Wireless communications, networking and mobile computing. Pp 1–5

  219. Zhang R, Ong SK, Nee AYC (2015) A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling. Appl Soft Comput 37:521–532

    Google Scholar 

  220. Zhang X-Y, Zhang J, Gong Y-J, Zhan Z-H, Chen W-N, Li Y (2016) Kuhn-Munkres parallel genetic algorithm for the set cover problem and its application to large-scale wireless sensor networks. IEEETrans Evol Comput 20(5):695–710

    Google Scholar 

  221. Zhenhua Y, Guangwen Y, Shanwei L, Qishan Z (2010) A modified immune genetic algorithm for channel assignment problems in cellular radio networks. In: Intelligent system design and engineering application (ISDEA), 2010 International Conference on, vol 2. , pp 823–826

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, National Institute of Technology, Hamirpur, India

    Sourabh Katoch, Sumit Singh Chauhan & Vijay Kumar

Authors
  1. Sourabh Katoch
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sumit Singh Chauhan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Vijay Kumar
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Vijay Kumar.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Katoch, S., Chauhan, S.S. & Kumar, V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80, 8091–8126 (2021). https://doi.org/10.1007/s11042-020-10139-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10139-6

Keywords