Time complexity estimation and optimisation of the genetic algorithm clustering method

Zulkifli Mohd Nopiah, M. I. Khairir, Shahrum Abdullah, M. N. Baharin, Azli Arifin

Research output: Contribution to journalArticle

4 Citations (Scopus)

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 twodimensional 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.

Original languageEnglish
Pages (from-to)334-344
Number of pages11
JournalWSEAS Transactions on Mathematics
Volume9
Issue number5
Publication statusPublished - May 2010

Fingerprint

Clustering Methods
Time Complexity
Cluster Analysis
Genetic algorithms
Genetic Algorithm
Clustering algorithms
Clustering Algorithm
Optimization
Kurtosis
Time Series Data
Fatigue
Time series
Inclusion
Polynomials
Fatigue of materials
Iteration
Polynomial
Population
Genetic algorithm
Clustering

Keywords

  • Algorithm efficiency
  • Big-O notation
  • Clustering
  • Fatigue damage
  • Genetic algorithms
  • Time complexity

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

Time complexity estimation and optimisation of the genetic algorithm clustering method. / Mohd Nopiah, Zulkifli; Khairir, M. I.; Abdullah, Shahrum; Baharin, M. N.; Arifin, Azli.

In: WSEAS Transactions on Mathematics, Vol. 9, No. 5, 05.2010, p. 334-344.

Research output: Contribution to journalArticle

@article{715283e61e384b14965b69419275f902,
title = "Time complexity estimation and optimisation of the genetic algorithm clustering method",
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 twodimensional 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.",
keywords = "Algorithm efficiency, Big-O notation, Clustering, Fatigue damage, Genetic algorithms, Time complexity",
author = "{Mohd Nopiah}, Zulkifli and Khairir, {M. I.} and Shahrum Abdullah and Baharin, {M. N.} and Azli Arifin",
year = "2010",
month = "5",
language = "English",
volume = "9",
pages = "334--344",
journal = "WSEAS Transactions on Mathematics",
issn = "1109-2769",
publisher = "World Scientific and Engineering Academy and Society",
number = "5",

}

TY - JOUR

T1 - Time complexity estimation and optimisation of the genetic algorithm clustering method

AU - Mohd Nopiah, Zulkifli

AU - Khairir, M. I.

AU - Abdullah, Shahrum

AU - Baharin, M. N.

AU - Arifin, Azli

PY - 2010/5

Y1 - 2010/5

N2 - 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 twodimensional 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.

AB - 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 twodimensional 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.

KW - Algorithm efficiency

KW - Big-O notation

KW - Clustering

KW - Fatigue damage

KW - Genetic algorithms

KW - Time complexity

UR - http://www.scopus.com/inward/record.url?scp=77954771611&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77954771611&partnerID=8YFLogxK

M3 - Article

VL - 9

SP - 334

EP - 344

JO - WSEAS Transactions on Mathematics

JF - WSEAS Transactions on Mathematics

SN - 1109-2769

IS - 5

ER -