The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON MATHEMATICS
Transactions ID Number: 89-553
Full Name: Zulkifli Mohd Nopiah
Position: Senior Lecturer
Age: ON
Sex: Male
Address: Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
Country: MALAYSIA
Tel:
Tel prefix:
Fax:
E-mail address: zmn@eng.ukm.my
Other E-mails: zmn1993@gmail.com
Title of the Paper: Time Complexity Estimation and Optimisation of the Genetic Algorithm Clustering Method
Authors as they appear in the Paper: Zulkifli Mohd Nopiah, Muhammad Ihsan Khairir, Shahrum Abdullah, Mohd Noor Baharin, Azli Arifin
Email addresses of all the authors: zmn@eng.ukm.my,mihsankk@vlsi.eng.ukm.my,shahrum@eng.ukm.my,baharin@vlsi.eng.ukm.my,azli@eng.ukm.my
Number of paper pages: 11
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.
Keywords: Genetic algorithms, Fatigue damage, Clustering, Time complexity, Big-O notation, Algorithm efficiency
EXTENSION of the file: .doc
Special (Invited) Session: Time Complexity Analysis of the Genetic Algorithm Clustering Method
Organizer of the Session: 640-651
How Did you learn about congress:
IP ADDRESS: 202.185.32.254
