Optimising real-time performance of genetic algorithm clustering method

Muhammad Ihsan Khairir, Zulkifli Mohd Nopiah, Shahrum Abdullah, Mohd Noor Baharin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the optimisation of real-time performance of the genetic algorithm clustering method. This performance optimisation concerns the population diversity and limitation and is based on actual runtime of the algorithm. A real-time ticker is incorporated into the algorithm for actual runtime measurement. For population diversity and limitation, a controlled k-means analysis is performed on the population of solutions to determine its diversity. Achieving a less diverse population in less amount of time without sacrificing the accuracy of the algorithm will help reduce the time-complexity of the algorithm, thus opening up the potential for the algorithm to cluster data in higher dimensions. Results from this study will be used for improving the method of clustering fatigue damage features of automotive components using genetic algorithm based methods.

Original languageEnglish
Title of host publicationKey Engineering Materials
Pages223-229
Number of pages7
Volume462-463
DOIs
Publication statusPublished - 2011
Event8th International Conference on Fracture and Strength of Solids 2010, FEOFS2010 - Kuala Lumpur
Duration: 7 Jun 20109 Jun 2010

Publication series

NameKey Engineering Materials
Volume462-463
ISSN (Print)10139826

Other

Other8th International Conference on Fracture and Strength of Solids 2010, FEOFS2010
CityKuala Lumpur
Period7/6/109/6/10

Fingerprint

Genetic algorithms
Fatigue damage

Keywords

  • Clustering
  • Diversity of solutions
  • Fatigue damage
  • Genetic algorithms
  • Optimisation

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

Cite this

Khairir, M. I., Mohd Nopiah, Z., Abdullah, S., & Baharin, M. N. (2011). Optimising real-time performance of genetic algorithm clustering method. In Key Engineering Materials (Vol. 462-463, pp. 223-229). (Key Engineering Materials; Vol. 462-463). https://doi.org/10.4028/www.scientific.net/KEM.462-463.223

Optimising real-time performance of genetic algorithm clustering method. / Khairir, Muhammad Ihsan; Mohd Nopiah, Zulkifli; Abdullah, Shahrum; Baharin, Mohd Noor.

Key Engineering Materials. Vol. 462-463 2011. p. 223-229 (Key Engineering Materials; Vol. 462-463).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Khairir, MI, Mohd Nopiah, Z, Abdullah, S & Baharin, MN 2011, Optimising real-time performance of genetic algorithm clustering method. in Key Engineering Materials. vol. 462-463, Key Engineering Materials, vol. 462-463, pp. 223-229, 8th International Conference on Fracture and Strength of Solids 2010, FEOFS2010, Kuala Lumpur, 7/6/10. https://doi.org/10.4028/www.scientific.net/KEM.462-463.223
Khairir MI, Mohd Nopiah Z, Abdullah S, Baharin MN. Optimising real-time performance of genetic algorithm clustering method. In Key Engineering Materials. Vol. 462-463. 2011. p. 223-229. (Key Engineering Materials). https://doi.org/10.4028/www.scientific.net/KEM.462-463.223
Khairir, Muhammad Ihsan ; Mohd Nopiah, Zulkifli ; Abdullah, Shahrum ; Baharin, Mohd Noor. / Optimising real-time performance of genetic algorithm clustering method. Key Engineering Materials. Vol. 462-463 2011. pp. 223-229 (Key Engineering Materials).
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