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A review on genetic algorithm: past, present, and future
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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.
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“GA, derived from a study of a biological system, can solve both constrained and unconstrained optimization problems based on a natural selection process [164,165].”
From Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review (2021) · cites this paper as [165]
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Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000097 · Cited by 64,042
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
Wang, G., Ma, R., Qiao, G., Wada, K., Aizawa, Y., & Satoh, T. (2015) · Integrative Medicine International · doi:10.1159/000368408 · Cited by 18,762
Genetic Algorithms + Data Structures = Evolution Programs
Michalewicz, Z. (1992) · Artificial intelligence · doi:10.1007/978-3-662-02830-8 · Cited by 11,581
An Introduction to Genetic Algorithms
Mitchell, M. (1996) · doi:10.7551/mitpress/3927.001.0001 · Cited by 11,340
Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
Srinivas, N., & Deb, K. (1994) · Evolutionary Computation · doi:10.1162/evco.1994.2.3.221 · Cited by 6,935
Bonabeau, E. W., Dorigo, M., & Théraulaz, G. (1999) · Oxford University Press eBooks · doi:10.1093/oso/9780195131581.001.0001 · Cited by 6,652
Dorigo, M., Birattari, M., & Stützle, T. (2006) · IEEE Computational Intelligence Magazine · doi:10.1109/ci-m.2006.248054 · Cited by 6,363
Dorigo, M., Birattari, M., & Stützle, T. (2006) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2006.329691 · Cited by 5,338
Simulated Binary Crossover for Continuous Search Space.
Deb, K., & Agrawal, R. B. (1995) · Complex Systems · Cited by 2,976
A niched Pareto genetic algorithm for multiobjective optimization
Horn, J., Nafpliotis, N., & Goldberg, D. E. (2002) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/icec.1994.350037 · Cited by 2,473
Evolutionary Algorithms for Constrained Parameter Optimization Problems
Michalewicz, Z., & Schoenauer, M. (1996) · Evolutionary Computation · doi:10.1162/evco.1996.4.1.1 · Cited by 1,719
Dhiman, G., & Kumar, V. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.11.024 · Cited by 1,220
Self-citationDhiman, G., & Kumar, V. (2017) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2017.05.014 · Cited by 1,066
Self-citationA multi-objective genetic local search algorithm and its application to flowshop scheduling
Ishibuchi, H., & Murata, T. (1998) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/5326.704576 · Cited by 1,028
Genetic Algorithms for Real Parameter Optimization
Wright, A. H. (1991) · Foundations of genetic algorithms · doi:10.1016/b978-0-08-050684-5.50016-1 · Cited by 1,006
Computational Intelligence and Neuroscience
(2022) · Computational Intelligence and Neuroscience · doi:10.1155/8483 · Cited by 961
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
Larrañaga, P., Kuijpers, C. M. H., Murga, R. H., Inza, I., & Dizdarevic, S. (1999) · Artificial Intelligence Review · doi:10.1023/a:1006529012972 · Cited by 849
Many-Objective Evolutionary Algorithms
Li, B., Li, J., Tang, K., & Yao, X. (2015) · ACM Computing Surveys · doi:10.1145/2792984 · Cited by 841
A tutorial on multiobjective optimization: fundamentals and evolutionary methods
Emmerich, M., & Deutz, A. H. (2018) · Natural Computing · doi:10.1007/s11047-018-9685-y · Cited by 812
Goldberg, D. E. (1989) · Medical Entomology and Zoology · Cited by 798
Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization
Koziel, S. M., & Michalewicz, Z. (1999) · Evolutionary Computation · doi:10.1162/evco.1999.7.1.19 · Cited by 761
Emperor penguin optimizer: A bio-inspired algorithm for engineering problems
Dhiman, G., & Kumar, V. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.06.001 · Cited by 730
Self-citationChoosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach
Hassanat, A. B. A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A., & Prasath, V. B. S. (2019) · Information · doi:10.3390/info10120390 · Cited by 673
Face Recognition Systems: A Survey
Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020) · Sensors · doi:10.3390/s20020342 · Cited by 623
MULTI-OBJECTIVE FUNCTION OPTIMIZATION USING NON-DOMINATED SORTING GENETIC ALGORITHMS
Srinivas, N., & Deb, K. (1994) · Cited by 585
Genetic local search for multi-objective combinatorial optimization
Jaszkiewicz, A. (2002) · European Journal of Operational Research · doi:10.1016/s0377-2217(01)00104-7 · Cited by 548
Yu, F., & Xiaozhong, X. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.07.104 · Cited by 539
2012 IEEE Congress on Evolutionary Computation
Abbass, H. A., Essam, D., & Sarker, R. A. (2012) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2012.6256101 · Cited by 536
Archives of Computational Methods in Engineering
(2013) · Archives of Computational Methods in Engineering · doi:10.1007/11831.1886-1784 · Cited by 528
Lee, Y., Hara, T., Fujita, H., Itoh, S., & Ishigaki, T. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.932744 · Cited by 457
A Micro-Genetic Algorithm for Multiobjective Optimization
Coello, C. A. C., & Toscano‐Pulido, G. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44719-9_9 · Cited by 425
A new crossover operator for real coded genetic algorithms
Deep, K., & Thakur, M. (2006) · Applied Mathematics and Computation · doi:10.1016/j.amc.2006.10.047 · Cited by 421
A new mutation operator for real coded genetic algorithms
Deep, K., & Thakur, M. (2007) · Applied Mathematics and Computation · doi:10.1016/j.amc.2007.03.046 · Cited by 391
Proceedings of the First International Conference on Genetic Algorithms and their Applications
Grefenstette, J. J. (2014) · Psychology Press eBooks · doi:10.4324/9781315799674 · Cited by 368
CROSSOVER OPERATORS IN GENETIC ALGORITHMS: A REVIEW
Umbarkar, A. J., & Sheth, P. D. (2015) · ICTACT Journal on Soft Computing · doi:10.21917/ijsc.2015.0150 · Cited by 364
Biases in the crossover landscape
Eshelman, L. J., Caruana, R. A., & Schaffer, J. D. (1989) · international conference on Genetic algorithms · Cited by 347
Fog Orchestration for Internet of Things Services
Wen, Z., Yang, R., Garraghan, P., Lin, T., Xu, J., & Rovatsos, M. (2017) · IEEE Internet Computing · doi:10.1109/mic.2017.36 · Cited by 339
A genetic algorithm approach for location-inventory-routing problem with perishable products
Hiassat, A., Diabat, A. H., & Rahwan, I. (2016) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2016.10.004 · Cited by 325
Introduction to Genetic Algorithm
Dutta, P., Pal, S., Kumar, A. R., & Cengiz, K. (2023) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003216001-5 · Cited by 308
Chaves, D., Fidalgo, E., Alegre, E., Aláiz-Rodríguez, R., Jáñez-Martino, F., & Azzopardi, G. (2020) · Sensors · doi:10.3390/s20164491 · Cited by 296
Chen, R., Liang, C., Hong, W., & Gu, D. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.10.022 · Cited by 293
Soleimani, H., & Govindan, K. (2014) · Applied Mathematical Modelling · doi:10.1016/j.apm.2014.12.016 · Cited by 292
Hybrid Genetic Algorithms: A Review
El-Mihoub, T. A., Hopgood, A. A., Nolle, L., et al. (2006) · Cited by 287
Analysing mutation schemes for real-parameter genetic algorithms
Deb, K., & Deb, A. (2014) · International Journal of Artificial Intelligence and Soft Computing · doi:10.1504/ijaisc.2014.059280 · Cited by 285
Fuzzy multi-objective sustainable and green closed-loop supply chain network design
Soleimani, H., Govindan, K., Saghafi, H., et al. (2017) · Computers & Industrial Engineering · doi:10.1016/j.cie.2017.04.038 · Cited by 285
SVR with hybrid chaotic genetic algorithms for tourism demand forecasting
Hong, W., Dong, Y., Chen, L.-Y., & Wei, S.-Y. (2010) · Applied Soft Computing · doi:10.1016/j.asoc.2010.06.003 · Cited by 268
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
Larrañaga, P., Kuijpers, C. M. H., Murga, R. H., & Yurramendi, Y. (1996) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/3468.508827 · Cited by 268
International Journal of Engineering and Computer Science
(2017) · International Journal Of Engineering And Computer Science · doi:10.18535/ijecs · Cited by 265
A parameter-less genetic algorithm
Harik, G. R., & Lobo, F. G. (1999) · Cited by 241
Farahani, R. Z., & Elahipanah, M. (2007) · International Journal of Production Economics · doi:10.1016/j.ijpe.2006.11.028 · Cited by 230
A review of applications of genetic algorithms in operations management
Lee, C. K. H. (2018) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2018.08.011 · Cited by 199
A genetic algorithm for cellular manufacturing design and layout
Wu, X., Chu, C., Wang, Y., et al. (2006) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.05.035 · Cited by 193
The Applications of Genetic Algorithms in Medicine
Ghaheri, A., Shoar, S., Naderan, M., et al. (2015) · Oman Medical Journal · doi:10.5001/omj.2015.82 · Cited by 188
A soft computing approach to localization in wireless sensor networks
Yun, S., Lee, J. H., Chung, W., et al. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.064 · Cited by 186
A hybrid genetic algorithm for the traveling salesman problem with drone
Ha, Q. M., Deville, Y., Pham, Q. D., et al. (2019) · Journal of Heuristics · doi:10.1007/s10732-019-09431-y · Cited by 183
Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator
Hussain, A., Shad, M. Y., Sajid, M. N., Hussain, I., Shoukry, A. M., & Gani, S. H. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/7430125 · Cited by 181
Yang, S., Cheng, H., & Wang, F. (2009) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2009.2023676 · Cited by 180
Crossover and Mutation Operators of Genetic Algorithms
Lim, S. M., Sultan, A. B. M., Sulaiman, M. N., Mustapha, A., & Leong, K. Y. (2017) · International Journal of Machine Learning and Computing · doi:10.18178/ijmlc.2017.7.1.611 · Cited by 176
Genetic Operators for Sequencing Problems
Fox, B. R., & Mcmahon, M. B. (1991) · Foundations of genetic algorithms · doi:10.1016/b978-0-08-050684-5.50021-5 · Cited by 170
New genetic local search operators for the traveling salesman problem
Freisleben, B., & Merz, P. (1996) · Lecture notes in computer science · doi:10.1007/3-540-61723-x_1052 · Cited by 167
Harada, T., & Alba, E. (2020) · ACM Computing Surveys · doi:10.1145/3400031 · Cited by 167
Single row facility layout problem using a permutation-based genetic algorithm
Datta, D., Amaral, A. R. S., & Figueira, J. R. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.03.034 · Cited by 166
A recent survey on image watermarking techniques and its application in e-governance
Kumar, C., Singh, A. K., & Kumar, P. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5222-8 · Cited by 156
Subbaraj, P., Rengaraj, R., & Salivahanan, S. (2009) · Applied Soft Computing · doi:10.1016/j.asoc.2009.10.019 · Cited by 148
Jiang, S., Chin, K., Wang, L., Qu, G., & Tsui, K. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.017 · Cited by 146
Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems
Kumar, V., Chhabra, J. K., & Kumar, D. (2013) · Journal of Computational Science · doi:10.1016/j.jocs.2013.12.001 · Cited by 139
Self-citationAzadeh, A., Elahi, S., Farahani, M. H., et al. (2016) · Computers & Industrial Engineering · doi:10.1016/j.cie.2016.12.019 · Cited by 137
Computational Intelligence and Neuroscience
(2008) · Computational Intelligence and Neuroscience · doi:10.1155/cin · Cited by 137
Aiello, G., Scalia, G. L., & Enea, M. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.125 · Cited by 131
Genetic algorithms in wireless networking: techniques, applications, and issues
Mehboob, U., Qadir, J., Ali, S., & Vasilakos, A. V. (2016) · Soft Computing · doi:10.1007/s00500-016-2070-9 · Cited by 128
Dhal, K. G., Ray, S., Das, A., & Das, S. K. (2018) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-018-9289-9 · Cited by 127
Abbasi, M., Rafiee, M., Khosravi, M. R., et al. (2020) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-020-0157-4 · Cited by 127
(2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3014328 · Cited by 127
Speed-based toll design for cordon-based congestion pricing scheme
Liu, Z., Meng, Q., & Wang, S. (2013) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2013.02.012 · Cited by 123
Kita, H., Ono, I., & Kobayashi, S. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.1999.782672 · Cited by 120
Multi-offspring genetic algorithm and its application to the traveling salesman problem
Wang, J., Ersoy, O. K., He, M., et al. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.02.021 · Cited by 120
Guido, R., & Conforti, D. (2016) · Computers & Operations Research · doi:10.1016/j.cor.2016.11.009 · Cited by 119
Şahingöz, Ö. K. (2013) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-013-9968-6 · Cited by 117
High performance genetic algorithm for land use planning
Porta, J., Parapar, J., Doallo, R., et al. (2012) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2012.05.003 · Cited by 114
An island model genetic algorithm for unequal area facility layout problems
Palomo-Romero, J. M., Salas‐Morera, L., & García‐Hernández, L. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.004 · Cited by 114
Soft computing approaches for image segmentation: a survey
Chouhan, S. S., Kaul, A., & Singh, U. P. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6005-6 · Cited by 114
Kia, R., Khaksar-Haghani, F., Javadian, N., et al. (2014) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2013.12.005 · Cited by 112
COLOR IMAGE SEGMENTATION WITH GENETIC ALGORITHM FOR IN-FIELD WEED SENSING
Tang, L. X., Tian, L. F., & Steward, B. L. (2000) · Transactions of the ASAE · doi:10.13031/2013.2970 · Cited by 112
Adaptive directed mutation for real-coded genetic algorithms
Tang, P.-H., & Tseng, M. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2012.08.035 · Cited by 111
Zhang, X., Zhang, J., Gong, Y., Zhan, Z., Chen, W., & Li, Y. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2511142 · Cited by 110
Review of Selection Methods in Genetic Algorithms
Saini, N. (2017) · International Journal Of Engineering And Computer Science · Cited by 110
Dynamic Crowding Distance?A New Diversity Maintenance Strategy for MOEAs
Luo, B., Zheng, J., Xie, J., et al. (2008) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2008.532 · Cited by 107
An effective new island model genetic algorithm for job shop scheduling problem
Kurdi, M. (2015) · Computers & Operations Research · doi:10.1016/j.cor.2015.10.005 · Cited by 105
Sermpinis, G., Stasinakis, C., Theofilatos, K., & Karathanasopoulos, A. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.06.052 · Cited by 104
Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence
Pelikán, M., Pelikán, M., Cantú‐Paz, E., & Goldberg, D. E. (2000) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 102
A genetic algorithm with the heuristic procedure to solve the multi-line layout problem
Sadrzadeh, A. (2012) · Computers & Industrial Engineering · doi:10.1016/j.cie.2011.12.033 · Cited by 100
Using Genetic Algorithms to Optimize ACS-TSP
Pilat, M. L., & White, T. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45724-0_28 · Cited by 96
Shayeghi, A., Götz, D. A., Davis, J. B. A., Schäfer, R., & Johnston, R. L. (2014) · Physical Chemistry Chemical Physics · doi:10.1039/c4cp04323e · Cited by 96
Molecular recognition using a binary genetic search algorithm
Payne, A. W. R., & Glen, R. C. (1993) · Journal of Molecular Graphics · doi:10.1016/0263-7855(93)87001-l · Cited by 94
Cheng, H., Yang, S., & Cao, J. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.08.050 · Cited by 90
A genetic algorithm for the vendor-managed inventory routing problem with lost sales
Park, Y.-B., Yoo, J.-S., & Park, H.-S. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.01.041 · Cited by 90
Tahir, M., Tubaishat, A., Al-Obeidat, F. N., et al. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05347-y · Cited by 89
Neto, J. C., Meyer, G. E., & Jones, D. D. (2006) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2005.11.002 · Cited by 88
Vitayasak, S., Pongcharoen, P., & Hicks, C. (2016) · International Journal of Production Economics · doi:10.1016/j.ijpe.2016.03.019 · Cited by 87
A simple and efficient real-coded genetic algorithm for constrained optimization
Chuang, Y., Chen, C., & Hwang, C. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.09.036 · Cited by 80
Quadratic approximation based hybrid genetic algorithm for function optimization
Deep, K., & Das, K. N. (2008) · Applied Mathematics and Computation · doi:10.1016/j.amc.2008.04.021 · Cited by 79
Kumar, V., & Kumar, D. (2017) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2017.05.008 · Cited by 78
Self-citationRoberge, V., Tarbouchi, M., & Okou, F. A. (2014) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2014.2311737 · Cited by 76
A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling
Zhang, R., Ong, S. K., & Nee, A. Y. C. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.08.051 · Cited by 74
A hybrid genetic algorithm based heuristic for an integrated supply chain problem
Diabat, A. H., & Deskoores, R. M. (2015) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2015.04.011 · Cited by 74
Yang, C., Li, H., Rezgui, Y., Petri, I., Yüce, B., Chen, B., & Jayan, B. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.02.053 · Cited by 72
Dynamic facility layout problem based on flexible bay structure and solving by genetic algorithm
Mazinani, M., Abedzadeh, M., & Mohebali, N. (2012) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-012-4229-6 · Cited by 69
Robust encodings in genetic algorithms: a survey of encoding issues
Ronald, S. (2002) · IEEE International Conference on Evolutionary Computation (ICEC) · doi:10.1109/icec.1997.592265 · Cited by 67
Beta Chaotic Map Based Image Encryption Using Genetic Algorithm
Kaur, M., & Kumar, V. (2018) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127418501328 · Cited by 67
Self-citationBalanced crossover operators in Genetic Algorithms
Manzoni, L., Mariot, L., & Tuba, E. (2020) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2020.100646 · Cited by 67
Ghosh, S., & Bhattacharya, S. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106692 · Cited by 67
Shi, J., Liu, Z., Tang, L., et al. (2016) · Applied Mathematical Modelling · doi:10.1016/j.apm.2016.11.004 · Cited by 66
Entropy-Boltzmann selection in the genetic algorithms
Lee, C.-Y. (2003) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2003.808184 · Cited by 65
Shukla, A. K., Singh, P., & Vardhan, M. (2019) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026819500202 · Cited by 65
Alves, M. J., & Almeida, M. (2006) · Computers & Operations Research · doi:10.1016/j.cor.2006.02.008 · Cited by 63
Cui, M., Li, L., & Shi, M. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/1240162 · Cited by 62
New operators of genetic algorithms for traveling salesman problem
Ray, S. S., Sanghamitra, B., & Pal, S. K. (2004) · Cited by 61
Piszcz, A., & Soule, T. (2006) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1143997.1144166 · Cited by 60
Implementation of Path Planning using Genetic Algorithms on Mobile Robots
Burchardt, H., & Salomon, R. (2006) · doi:10.1109/cec.2006.1688529 · Cited by 60
Optimal Routing and Traffic Scheduling for Multihop Cellular Networks Using Genetic Algorithm
Lorenzo, B., & Glisic, S. (2012) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2012.204 · Cited by 58
Solving very large instances of the scheduling of independent tasks problem on the GPU
Pinel, F., Dorronsoro, B., & Bouvry, P. (2012) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2012.02.018 · Cited by 57
A Hybrid Optimization Technique Using Exchange Market and Genetic Algorithms
Jafari, A., Khalili, T., Babaei, E., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2962153 · Cited by 57
Using Micro-Genetic Algorithms to Improve Localization in Wireless Sensor Networks
Tam, V. W. L., Cheng, K., & Lui, K. (2006) · Journal of Communications · doi:10.4304/jcm.1.4.1-10 · Cited by 56
Airline flight schedule planning under competition
Abdelghany, A. F., Abdelghany, K. F., & Azadian, F. (2017) · Computers & Operations Research · doi:10.1016/j.cor.2017.05.013 · Cited by 55
Applications of Evolutionary Computation
Sim, K., Kaufmann, P., Ascheid, G., Bacardit, J., Cagnoni, S., Cotta, C., D’Andreagiovanni, F., Divina, F., Esparcia-Alcázar, A. I., Vega, F. F. D., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77538-8 · Cited by 53
Hybrid genetic algorithm for the open capacitated arc routing problem
Arakaki, R. K., & Usberti, F. L. (2017) · Computers & Operations Research · doi:10.1016/j.cor.2017.09.020 · Cited by 52
Ekbatanifard, G., Monsefi, R., Akbarzadeh-T, M.-R., et al. (2010) · doi:10.1109/iswpc.2010.5483775 · Cited by 51
Toward accurate localization and high recognition performance for noisy iris images
Wang, N., Li, Q., El‐Latif, A. A. A., Zhang, T., & Niu, X. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1278-7 · Cited by 51
A genetic algorithm for the design of a fuzzy controller for active queue management
Fatta, G. D., Hoffmann, F., Re, G. L., et al. (2003) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2003.818946 · Cited by 50
Stochastic imaging of soil parameters to assess variability and uncertainty of crop yield estimates
Pachepsky, Y. A., & Acock, B. (1998) · Geoderma · doi:10.1016/s0016-7061(98)00021-4 · Cited by 49
An effective parallel approach for genetic-fuzzy data mining
Hong, T., Lee, Y.-C., & Wu, M.-T. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.07.090 · Cited by 49
Yüce, B., Fruggiero, F., Packianather, M., et al. (2017) · Computers & Industrial Engineering · doi:10.1016/j.cie.2017.07.018 · Cited by 49
System identification and linearisation using genetic algorithms with simulated annealing
Tan, K. C. (1995) · doi:10.1049/cp:19951043 · Cited by 47
A genetic algorithm for supply chain configuration with new product development
Afrouzy, Z. A., Nasseri, S. H., & Mahdavi, I. (2016) · Computers & Industrial Engineering · doi:10.1016/j.cie.2016.09.008 · Cited by 47
ENCODING SCHEMES IN GENETIC ALGORITHM
Kumar, A. (2013) · Cited by 46
Improved real coded genetic algorithm for dynamic economic dispatch
Pattanaik, J. K., Basu, M., & Dash, D. P. (2018) · Journal of Electrical Systems and Information Technology · doi:10.1016/j.jesit.2018.03.002 · Cited by 44
Cheng, H., & Yang, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12239-2_58 · Cited by 43
Chaos genetic algorithm instead genetic algorithm.
Javidi, M. M., & Fard, R. H. (2015) · The International Arab Journal of Information Technology · Cited by 43
A new encoding scheme for solving job shop problems by genetic algorithm
Shi, G., Iima, H., & Sannomiya, N. (2002) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1996.577484 · Cited by 42
Parallel non-dominated sorting genetic algorithm-II-based image encryption technique
Kaur, M., & Kumar, V. (2018) · The Imaging Science Journal · doi:10.1080/13682199.2018.1505327 · Cited by 41
Self-citationAutomatic video segmentation using genetic algorithms
Kim, E. Y., & Park, S. H. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.07.023 · Cited by 40
An approach based on hybrid genetic algorithm applied to image denoising problem
de Paiva, J. L., Toledo, C. F. M., & Pedrini, H. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.09.013 · Cited by 40
Chaotic Genetic Algorithm based on Lorenz Chaotic System for Optimization Problems
Ebrahimzadeh, R., & Jampour, M. (2013) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2013.05.03 · Cited by 39
A comparison on the performance of crossover techniques in video game
Soon, G. K., Guan, T. T., On, C. K., et al. (2013) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce.2013.6720015 · Cited by 39
Fourier–Mellin moment-based intertwining map for image encryption
Kaur, M., & Kumar, V. (2018) · Modern Physics Letters B · doi:10.1142/s0217984918501154 · Cited by 39
Self-citationA Parallel Genetic Algorithm Framework for Transportation Planning and Logistics Management
Arkhipov, D. I., Wu, D., Wu, T., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2997812 · Cited by 39
Supply Chain Coordination under Trade Credit and Quantity Discount with Sales Effort Effects
Wang, Z., & Liu, S. (2018) · Mathematical Problems in Engineering · doi:10.1155/2018/2190236 · Cited by 38
Localization Algorithm for Wireless Sensor Network Based on Genetic Simulated Annealing Algorithm
Zhang, Q., Wang, J., Jin, C., et al. (2008) · International Conference on Wireless Communications, Networking and Mobile Computing · doi:10.1109/wicom.2008.850 · Cited by 34
Architecture and Performance of an Island Genetic Algorithm-Based Cognitive Network
Friend, D. H., ElNainay, M. Y., Shi, Y., & MacKenzie, A. B. (2008) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc08.2007.228 · Cited by 34
Chaotic genetic algorithm and the effects of entropy in performance optimization
Fuertes, G., Vargas, M., Alfaro, M. D., et al. (2019) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5048299 · Cited by 33
New operators of genetic algorithms for traveling salesman problem
Ray, S. S., Bandyopadhyay, S., & Pal, S. K. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334276 · Cited by 33
A call admission control scheme using genetic algorithms
Karabudak, D., Hung, C., & Bing, B. (2004) · ACM Symposium on Applied Computing · doi:10.1145/967900.968135 · Cited by 32
Load-balanced CDS construction in wireless sensor networks via genetic algorithm
He, J., Ji, S., Yan, M., et al. (2012) · International Journal of Sensor Networks · doi:10.1504/ijsnet.2012.046331 · Cited by 32
A Framework for Prediction Using Rough Set and Real Coded Genetic Algorithm
Rathi, R., & Acharjya, D. P. (2017) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-017-2838-y · Cited by 32
Mathematical modeling analysis of genetic algorithms under schema theorem
Liu, D. (2019) · Journal of Computational Methods in Sciences and Engineering · doi:10.3233/jcm-191019 · Cited by 32
An efficient multi-objective QoS-routing algorithm for wireless multicasting
Roy, A., Banerjee, N., & Das, S. K. (2003) · IEEE Vehicular Technology Conference (VTC-Spring) · doi:10.1109/vtc.2002.1002796 · Cited by 31
A hybrid genetic algorithm approach for backbone design of communication networks
Konak, A., & Smith, A. E. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.1999.785495 · Cited by 30
Unraveling Travelling Salesman Problem by genetic algorithm using m-crossover operator
Mudaliar, D. N., & Modi, N. (2013) · doi:10.1109/icsipr.2013.6497974 · Cited by 28
Energy cost optimization in irrigation system of smart farm by using genetic algorithm
Ocampo, A. L. P. D., & Dadios, E. P. (2017) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem.2017.8269497 · Cited by 28
A Fast Parallel Genetic Algorithm Based Approach for Community Detection in Large Networks
Ghoshal, A. K., Das, N., Bhattacharjee, S., et al. (2019) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets.2019.8711127 · Cited by 28
Performance of hybrid genetic algorithms incorporating local search
El-Mihoub, T. A., Hopgood, A. A., Nolle, L., et al. (2004) · Cited by 26
A Rule Based Classification for Vegetable Production Using Rough Set and Genetic Algorithm
Rathi, R., & Acharjya, D. P. (2017) · International Journal of Fuzzy System Applications · doi:10.4018/ijfsa.2018010106 · Cited by 26
Kannan, S. (2020) · Signal Image and Video Processing · doi:10.1007/s11760-019-01619-w · Cited by 26
(2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3010656 · Cited by 26
Kavitha, A. R., & Chellamuthu, C. (2016) · The Imaging Science Journal · doi:10.1080/13682199.2016.1178412 · Cited by 25
Khan, A., Rehman, Z. U., Jaffar, M. A., et al. (2019) · Signal Image and Video Processing · doi:10.1007/s11760-019-01419-2 · Cited by 24
Wang, J., Zhang, M., Ersoy, O. K., et al. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/4243853 · Cited by 24
Improved precedence preservation crossover for multi-objective job shop scheduling problem
Ripon, K. S. N., Siddique, N. H., & Tørresen, J. (2010) · Evolving Systems · doi:10.1007/s12530-010-9022-x · Cited by 23
Simplex Coding Genetic Algorithm for the Global Optimization of Nonlinear Functions
Hedar, A.-R., & Fukushima, M. (2003) · Springer eBooks · doi:10.1007/978-3-540-36510-5_17 · Cited by 22
Reliability-Aware Genetic Scheduling Algorithm in Grid Environment
Abdulal, W., & Ramachandram, S. (2011) · International Conference on Communication Systems and Network Technologies · doi:10.1109/csnt.2011.145 · Cited by 22
Parallel multi-objective metaheuristics for smart communications in vehicular networks
Toutouh, J., & Alba, E. (2015) · Soft Computing · doi:10.1007/s00500-015-1891-2 · Cited by 22
Kaluri, R., & Ch, P. (2016) · International journal of intelligent engineering and systems · doi:10.22266/ijies2016.1231.24 · Cited by 22
Wireless LAN load balancing with genetic algorithms
Scully, T., & Brown, K. N. (2009) · Knowledge-Based Systems · doi:10.1016/j.knosys.2008.10.008 · Cited by 21
Genetic algorithms for video segmentation
Kim, E. Y., & Jung, K. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2004.06.004 · Cited by 21
Singh, A., & Deep, K. (2015) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2015.12.01 · Cited by 21
Parallel Genetic Algorithms with GPU Computing
Cheng, J. R., & Gen, M. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.89152 · Cited by 21
Shuffle crossover and mutual information
Burkowski, F. J. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.1999.782671 · Cited by 20
Efficient face and gesture recognition techniques for robot control
Hu, C., Wang, X., Mandal, M. K., et al. (2004) · doi:10.1109/ccece.2003.1226250 · Cited by 20
A computer application for a bus body assembly line using Genetic Algorithms
Palencia, A. E. R., & Mejía, G. (2012) · International Journal of Production Economics · doi:10.1016/j.ijpe.2012.06.026 · Cited by 19
Wang, J., Cheng, Z., Ersoy, O. K., et al. (2018) · Mathematical Problems in Engineering · doi:10.1155/2018/5760841 · Cited by 19
Solving channel allocation problem in cellular radio networks using genetic algorithm
Pinagapany, S., & Kulkarni, A. V. (2008) · doi:10.1109/comswa.2008.4554416 · Cited by 18
Effective hybrid genetic algorithm for removing salt and pepper noise
Alaoui, N., Adamou‐Mitiche, A. B. H., & Mitiche, L. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2019.0566 · Cited by 18
Modified Evolutionary Algorithm and Chaotic Search for Bilevel Programming Problems
Abo-Elnaga, Y., & Nasr, S. (2020) · Symmetry · doi:10.3390/sym12050767 · Cited by 18
An Automatic Document Classifier System Based on Genetic Algorithm and Taxonomy
Diaz-Manríquez, A., Rios-Alvarado, A. B., Barrón-Zambrano, J. H., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2815992 · Cited by 17
Modelling and Simulation of Hydro Power Plant using MATLAB & WatPro 3.0
Tiwari, J., Singh, A. K., Yadav, A. K., et al. (2015) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2015.08.01 · Cited by 16
Kandavanam, G., Botvich, D., Balasubramaniam, S., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14156-0_5 · Cited by 16
Huo, P., Shiu, S., Wang, H., et al. (2009) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2009.552 · Cited by 16
SOLVING NON-LINEAR OPTIMIZATION PROBLEMS USING PARALLEL GENETIC ALGORITHM
Al-Oqaily, A. T., & Shakah, G. H. (2018) · doi:10.1109/csit.2018.8486176 · Cited by 16
A genetic algorithm-based approach for solving the resource-sharing and scheduling problem
Pinto, G., Ainbinder, I., & Rabinowitz, G. (2009) · Computers & Industrial Engineering · doi:10.1016/j.cie.2009.05.003 · Cited by 15
FPGA implementation of 2D cross-correlation for real-time 3D tracking of deformable surfaces
HajiRassouliha, A., Gamage, T. P. B., Parker, M. D., Nash, M. P., Taberner, A. J., & Nielsen, P. M. F. (2013) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2013.6727067 · Cited by 15
A Direction-Based Exponential Mutation Operator for Real-Coded Genetic Algorithm
Das, A. K., & Pratihar, D. K. (2018) · International Conference on Emerging Applications of Information Technology · doi:10.1109/eait.2018.8470407 · Cited by 15
Optimal Design of Experiments for Precision Agriculture Using a Genetic Algorithm
Peerlinck, A., Sheppard, J. W., Pastorino, J., et al. (2019) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2019.8790267 · Cited by 15
Evaluation of Accidental Death Records Using Hybrid Genetic AlgorithmPreprint
Sharma, N., Kaushik, I., Rathi, R., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3563084 · Cited by 15
Lima, S. J. D. A., & de Araújo, S. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91641-5_15 · Cited by 13
Research works printed but not linked: 11
- Abdullah J (2010) Multiobjectives ga-based QoS routing protocol for mobile ad hoc network. Int J Grid Distrib Comput 3(4):57–68
- 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.
- Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: ICGA, pp 416–423. Morgan Kaufmann
- 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
- Jebari K (2013) Selection methods for genetic algorithms. Abdelmalek Essaâdi University. International Journal of Emerging Sciences 3(4):333–344
- Leng LT (1999) Guided genetic algorithm. University of Essex, Doctoral Dissertation
- 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
- 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
- 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
- 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
- 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
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Metadata
- Authors
- Katoch, S., Chauhan, S. S., & Kumar, V.
- Affiliations
- National Institute of Technology Hamirpur (BD)
- Corresponding author
- Kumar, Vijay
- ORCID
- Kumar, Vijay
- Venue
- Multimedia Tools and Applications
- Published
- 2020-10-31
- Volume
- 80
- Issue
- 5
- Pages
- 8091-8126
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- PMC
- PMC7599983
- OpenAlex
- W3094704314
- Cited by
- 4,655
- References
- 231
- Citations by year
- 2026: 596 · 2025: 1098 · 2024: 1173 · 2023: 1039 · 2022: 596 · 2021: 151 · 2020: 1
- FWCI
- 175.83
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Multimedia Tools and Applications (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Antenna Design and Optimization
- Keywords
- genetic algorithm, genetic operators, fitness function, hybrid algorithm, evolutionary computation, future research directions
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