Journal
SCIENCEASIA
Volume 40, Issue -, Pages 78-86Publisher
SCIENCE SOCIETY THAILAND
DOI: 10.2306/scienceasia1513-1874.2014.40S.078
Keywords
moving average; maximum likelihood; moment estimation; steepest descent algorithm
Categories
Funding
- University of Malaya under the UMRG research grant [RG111-12ICT]
Ask authors/readers for more resources
In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function ln L(phi(1), theta(1)) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (phi(1), theta(1)) for the ARMA(1,1) model. Another comparison has been conducted to show that the I-CGA-SDA has fewer function evaluations, minimum search space percentage, faster convergence speed and has a higher optimal precision than that of the HGA.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available