Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Big Omicron and big Omega and big Theta
ACM SIGACT News
Constructive Genetic Algorithm for Clustering Problems
Evolutionary Computation
Adaptive Clustering Technique Using Genetic Algorithms
IEICE - Transactions on Information and Systems
Segmentation and scattering of fatigue time series data by kurtosis and root mean square
SIP'08 Proceedings of the 7th WSEAS International Conference on Signal Processing
Complexity analysis of Reed-Solomon decoding over GF(2m) without using syndromes
EURASIP Journal on Wireless Communications and Networking - Advances in Error Control Coding Techniques
A novel clustering algorithm based on the extension theory and genetic algorithm
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Genetic algorithm-based clustering approach for k-anonymization
Expert Systems with Applications: An International Journal
Peak-valley segmentation algorithm for fatigue time series data
WSEAS Transactions on Mathematics
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
A Weighted Genetic Algorithm Based Method for Clustering of Heteroscaled Datasets
ICSPS '09 Proceedings of the 2009 International Conference on Signal Processing Systems
Randomly shifted lattice rules on the unit cube for unbounded integrands in high dimensions
Journal of Complexity - Special issue: Algorithms and complexity for continuous problems Schloss Dagstuhl, Germany, September 2004
Application of genetic algorithm for designing cellular manufacturing system incrementally
WSEAS Transactions on Computers
Hi-index | 0.00 |
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.