A Genetic Algorithm Based Clustering Optimization A Survey


  • Rawaa Nadhum Saeed university babylon
  • Mahdi Abed Salman University of Babylon
  • Muhammed Abaid Mahdi University of Babylon




Parallel computing, Destributed network, Genetic algorithm, Optimization method


Genetic algorithm has an important role in improving clustering methods. When it comes to getting into a dataset, one of the most common methods used is clustering. Recent years have seen a rise in the number of articles interested in clustering, which may be attributed to the development of various new fields of application. Most clustering method's performance depends on initial values of parameters or the preprocessing of a dataset, so clustering needs to optimize. The improvement of clustering in two directions, either we improve the parameters or improve the input data. Ga is an evolutionary technique well-known in optimization. This survey deals with the methods that use a genetic algorithm to choose parameter values and input data. This study shows many important performance metrics used to get the optimal result in each research.


Download data is not yet available.


Gan, G., C. Ma, and J. Wu, Data clustering: theory, algorithms, and applications. 2020: SIAM. DOI: https://doi.org/10.1137/1.9781611976335

Rencher, A.C., A review of “Methods of Multivariate Analysis, ”. 2005, Taylor & Francis. DOI: https://doi.org/10.1080/07408170500232784

Gu, L. A novel locality-sensitive k-means clustering algorithm based on subtractive clustering. in 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS). 2016. IEEE.

Katoch, S., S.S. Chauhan, and V. Kumar, A review on the genetic algorithm: past, present, and future. Multimedia Tools and Applications, 2021. 80(5): p. 8091-8126. DOI: https://doi.org/10.1007/s11042-020-10139-6

Ihsan, M.A., M. Zarlis, and P. Sirait, Reduction Attributes on K-Nearest Neighbor Algorithm (KNN) using Genetic Algorithm. 2021.

Tabassum, M. and K. Mathew, A genetic algorithm analysis towards optimization solutions. International Journal of Digital Information and Wireless Communications (IJDIWC), 2014. 4(1): p. 124-142. DOI: https://doi.org/10.17781/P001091

Zeebaree, D.Q., et al., Combination of K-means clustering with Genetic Algorithm: A review. International Journal of Applied Engineering Research, 2017. 12(24): p. 14238-14245.

Sheikh, R.H., M.M. Raghuwanshi, and A.N. Jaiswal, Genetic Algorithm Based Clustering: A Survey. 2008: p. 314-319. DOI: https://doi.org/10.1109/ICETET.2008.48

Maulik, U. and S. Bandyopadhyay, Genetic algorithm-based clustering technique. Pattern recognition, 2000. 33(9): p. 1455-1465. DOI: https://doi.org/10.1016/S0031-3203(99)00137-5

Sumbherwal, N. and B. Hooda, Genetic algorithm-based clustering methods: A review. 2021.

Almadhoun, W. and M. Hamdan Optimizing the self-organizing team size using a genetic algorithm in agile practices. Journal of Intelligent Systems, 2020. 29(1): p. 1151-1165. DOI: https://doi.org/10.1515/jisys-2018-0085

Haryati, A.E. and S. Surono, COMPARATIVE STUDY OF DISTANCE MEASURES ON FUZZY SUBTRACTIVE CLUSTERING. MEDIA STATISTIKA.2020. 14(2): p. 137-145. DOI: https://doi.org/10.14710/medstat.14.2.137-145

Kour, H., J. Manhas, and V. Sharma, Evaluation of Subtractive Clustering-based Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means based ANFIS System in Diagnosis of Alzheimer. Journal of Multimedia Information System, 2019. 6(2): p. 87-90. DOI: https://doi.org/10.33851/JMIS.2019.6.2.87

Lambora, A., K. Gupta, and K. Chopra. Genetic algorithm-A literature review. in 2019 international conference on machine learning, big data, cloud, and parallel computing (COMITCon). 2019. IEEE. DOI: https://doi.org/10.1109/COMITCon.2019.8862255

Irwandi, O.S. Sitompul, and R.W. Sembiring, Performance Analysis of Subtractive Clustering Algorithm in Determining the Number and Position of Cluster Centers. Randwick International of Social Science Journal, 2021. 2(2): p. 193-199. DOI: https://doi.org/10.47175/rissj.v2i2.241

Lu, Y., et al., Incremental genetic K-means algorithm and its application in gene expression data analysis. BMC bioinformatics, 2004. 5(1): p. 1-10. DOI: https://doi.org/10.1186/1471-2105-5-1

Ganapathy, S., et al., A novel weighted fuzzy C–means clustering based on immune genetic algorithm for intrusion detection. Procedia Engineering, 2012. 38: p. 1750-1757. DOI: https://doi.org/10.1016/j.proeng.2012.06.213

Kala, R., A. Shukla, and R. Tiwary, A novel approach to clustering using genetic algorithm. International Journal of Engineering Research and Industrial Applications, 2010. 3(1): p. 81-88.

Ding, Y. and X. Fu, Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm. Neurocomputing, 2016. 188: p. 233-238. DOI: https://doi.org/10.1016/j.neucom.2015.01.106

Irfan, S., G. Dwivedi, and S. Ghosh. Optimization of K-means clustering using genetic algorithm. in 2017 International conference on computing and communication technologies for the smart nation (IC3TSN). 2017. IEEE. DOI: https://doi.org/10.1109/IC3TSN.2017.8284468

El-Shorbagy, M.A., et al. A novel genetic algorithm based k-means algorithm for cluster analysis. in International Conference on Advanced Machine Learning Technologies and Applications. 2018. Springer. DOI: https://doi.org/10.1007/978-3-319-74690-6_10

Nguyen, H., S.J. Louis, and T. Nguyen. MGKA: A genetic algorithm-based clustering technique for genomic data. in 2019 IEEE Congress on Evolutionary Computation (CEC). 2019. IEEE. DOI: https://doi.org/10.1109/CEC.2019.8790225

Alata, M., M. Molhim, and A. Ramini, Optimizing of fuzzy c-means clustering algorithm using GA. Update, 2008. 1(5). DOI: https://doi.org/10.19026/rjaset.5.5011

Le, T., T. Altman, and K.J. Gardiner. A fuzzy clustering method using Genetic Algorithm and Fuzzy Subtractive Clustering. in Proceedings of the International Conference on Information and Knowledge Engineering (IKE). 2012. The Steering Committee of The World Congress in Computer Science, Computer ….

Shieh, H.-L., P.-L. Chang, and C.-N. Lee. An efficient method for estimating cluster radius of subtractive clustering based on a genetic algorithm. in 2013 IEEE International Symposium on Consumer Electronics (ISCE). 2013. IEEE. DOI: https://doi.org/10.1109/ISCE.2013.6570150

Rahman, M., A., and M.Z. Islam, A hybrid clustering technique combining a novel genetic algorithm with K-Means. Knowledge-Based Systems, 2014. 71: p. 345-365. DOI: https://doi.org/10.1016/j.knosys.2014.08.011

Le, T. and L. Vu. A novel fuzzy clustering method based on GA, PSO, and Subtractive Clustering. in 2020 International Conference on Computational Science and Computational Intelligence (CSCI). 2020. IEEE. DOI: https://doi.org/10.1109/CSCI51800.2020.00063

Lubis, Z., P. Sihombing, and H. Mawengkang. Optimization of K Value at the K-NN algorithm in clustering using the expectation-maximization algorithm. in IOP Conference Series: Materials Science and Engineering. 2020. IOP Publishing. DOI: https://doi.org/10.1088/1757-899X/725/1/012133

Jebari, K., A. Elmoujahid, and A. Ettouhami, Automatic genetic fuzzy c-means. Journal of Intelligent Systems, 2020. 29(1): p. 529-539. DOI: https://doi.org/10.1515/jisys-2018-0063

Larose, D.T., An introduction to data mining. Traduction et adaptation de Thierry Vallaud, 2005.

Mukhopadhyay, A. and U. Maulik, Towards improving fuzzy clustering using support vector machine: Application to gene expression data. Pattern Recognition, 2009. 42(11): p. 2744-2763. DOI: https://doi.org/10.1016/j.patcog.2009.04.018

Zanaganeh, M., S.J. Mousavi, and A.F.E. Shahidi, A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters. Engineering Applications of Artificial Intelligence, 2009. 22(8): p. 1194-1202. DOI: https://doi.org/10.1016/j.engappai.2009.04.009

Halder, A., S. Pramanik, and A. Kar, Dynamic image segmentation using fuzzy c-means based genetic algorithm. International Journal of Computer Applications, 2011. 28(6): p. 15-20. DOI: https://doi.org/10.5120/3392-4714

Ghosh, A., N.S. Mishra, and S. Ghosh, Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, 2011. 181(4): p. 699-715. DOI: https://doi.org/10.1016/j.ins.2010.10.016

Lianjiang, Z., Q. Shouning, and D. Tao. Adaptive fuzzy clustering based on genetic algorithm. in 2010 2nd International Conference on Advanced Computer Control. 2010. IEEE.

Liu, Y. and Y. Zhang. Optimizing parameters of the fuzzy c-means clustering algorithm. in Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). 2007. IEEE. DOI: https://doi.org/10.1109/FSKD.2007.436

Davies, D. and D. Bouldin, A cluster separation measure, IEEE transactions on patter analysis and machine intelligence. vol. 1979, PAMI-1. DOI: https://doi.org/10.1109/TPAMI.1979.4766909

Rousseeuw, P.J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 1987. 20: p. 53-65. DOI: https://doi.org/10.1016/0377-0427(87)90125-7




How to Cite

Saeed, R. N., Salman, M. A., & Mahdi, M. A. (2023). A Genetic Algorithm Based Clustering Optimization A Survey . Journal of Kufa for Mathematics and Computer, 10(1), 42–48. https://doi.org/10.31642/JoKMC/2018/100105

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.