Time complexity estimation and optimisation of the genetic algorithm clustering method
Zulkifli Mohd Nopiah, Muhammad Ihsan Khairir, Shahrum Abdullah, Mohd Noor Baharin, Ahmad Mubarak Tajul Arifin · 2010
Abstract:- This paper presents the time complexity estimation and optimisation of the genetic algorithm clustering method. The tested feature in the clustering algorithm is the population limit function. For the purpose of the study, segmental kurtosis analysis was done on several segmented fatigue time series data, which are then represented in two-dimensional heteroscaled datasets. These datasets are then clustered using the genetic algorithm clustering method and the runtime of the algorithm is measured against the number of iterations. Polynomial fitting is used on the runtime data to determine the time complexity of the algorithm. Analysis is repeated with the inclusion of the population limit in the clustering algorithm. The results of the analysis will be used to determine the significance of including the population limit function in the algorithm for optimal performance.