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Abd El-Wahed, W.F., Mousa, A.A. and El-Shorbagy, M.A. (2011) Integrating Particle Swarm Optimization with Genetic Algorithms for Solving Nonlinear Optimization Problems. Journal of Computational and Applied Mathematics, 235, 1446-1453. http://dx.doi.org/10.1016/j.cam.2010.08.030

has been cited by the following article:

• TITLE: Hybrid Genetic Algorithm with K-Means for Clustering Problems

  AUTHORS: Ahamed Al Malki, Mohamed M. Rizk, M. A. El-Shorbagy, A. A. Mousa

  KEYWORDS: Cluster Analysis, Genetic Algorithm, K-Means

  JOURNAL NAME: Open Journal of Optimization, Vol.5 No.2, June 21, 2016

  ABSTRACT: The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim.