Paper Title:
Optimising Real-Time Performance of Genetic Algorithm Clustering Method
  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.

  Info
Periodical
Key Engineering Materials (Volumes 462-463)
Edited by
Ahmad Kamal Ariffin, Shahrum Abdullah, Aidy Ali, Andanastuti Muchtar, Mariyam Jameelah Ghazali and Zainuddin Sajuri
Pages
223-229
DOI
10.4028/www.scientific.net/KEM.462-463.223
Citation
M. I. Khairir, Z. M. Nopiah, S. Abdullah, M. N. Baharin, "Optimising Real-Time Performance of Genetic Algorithm Clustering Method", Key Engineering Materials, Vols. 462-463, pp. 223-229, 2011
Online since
January 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Hui Qin Sun, Zhi Hong Xue, Ke Jun Sun, Su Zhi Wang, Yun Du
Chapter 2: Manufacturing Technology
Abstract:BP neural network is currently the most widely used of neural network models in practical application in transformer fault diagnosis. BP...
789
Authors: Na Rui Bu, Run Shan Bai, Zhang Zhen Li, De Zhong Lin
Chapter 6: Vibration, Noise Analysis and Control
Abstract:Analysis of slope stability based on BP neural network, the analytical model of slope stability is built. Aiming at the defects that BP...
1263
Authors: Si Lian Xie, Tie Bin Wu, Shui Ping Wu, Yun Lian Liu
Chapter 18: Computer Applications in Industry and Engineering
Abstract:Evolutionary algorithms are amongst the best known methods of solving difficult constrained optimization problems, for which traditional...
2846
Authors: Rui Ni Li, Xiao Yi Wang, Zai Wen Liu, Ji Ping Xu, Ling Bin Wang
Chapter 4: Waste Disposal and Recycling
Abstract:Various unusual conditions are likely to occur during sewage treatment process, which would lead to some consequences such as the decrease of...
622
Authors: Hai Yan Wang
Chapter 6: Production Management
Abstract:This paper presents a hybrid algorithm to address the flexible job-shop scheduling problem (FJSP). Based on Differential Evolution (DE), a...
502