Time complexity analysis of the genetic algorithm clustering method

  • Authors:

  • Z. M. Nopiah;M. I. Khairir;S. Abdullah;M. N. Baharin;A. Arifin

  • Affiliations:

  • Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

  • Venue:
  • ISPRA'10 Proceedings of the 9th WSEAS international conference on Signal processing, robotics and automation
  • Year:
  • 2010

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Abstract

This paper presents the time complexity analysis 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 at 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. The results of the analysis will be used to determine the significance of including the population limit function in the algorithm for optimal performance.