AlDabbagh, R.D. and Baba, M.S. and Mekhilef, Saad and Kinsheel, A. (2012) The compact genetic algorithm for likelihood estimator of first order moving average model. In: 2012 2nd International Conference on Digital Information and Communication Technology and its Applications, DICTAP 2012, Bangkok.

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Abstract
Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results based on MSE were compared with those obtained from the moments method and showed that the Canonical GA and compact GA can give good estimator of Î¸ for the MA(1) model. Another comparison has been conducted to show that the cGA method has less number of function evaluations, minimum searched space percentage, faster convergence speed and has a higher optimal precision than that of the Canonical GA.
Item Type:  Conference or Workshop Item (Paper) 

Additional Information:  Conference code: 91071 Export Date: 16 November 2012 Source: Scopus Art. No.: 6215410 doi: 10.1109/DICTAP.2012.6215410 Language of Original Document: English Correspondence Address: AlDabbagh, R.D.; Department of Artificial Intelligence, University of Malaya, Kuala Lumpur, Malaysia; email: rawaaaldabbagh@siswa.um.edu.my References: Goldberg David, E., (1993) Genetic Algorithms in Search, Optimization and Machine Learning, , Reprinted with corrections from Goldberg (1989) Addison Wesley Longman Inc; Larranaga, P., Lozano, J.A., (2002) Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, 2. , Springer Netherlands; Pelikan, M., Goldberg, D.E., Lobo, F.G., A survey of optimization by building and using probabilistic models (2002) Computational Optimization and Applications, 21 (1), pp. 520. , DOI 10.1023/A:1013500812258; Rastegar, R., Hariri, A., A step forward in studying the compact genetic algorithm (2006) Evolutionary Computation, 14 (3), pp. 277289. , http://www.mitpressjournals.org/doi/pdf/10.1162/evco.2006.14.3.277, DOI 10.1162/evco.2006.14.3.277; Baluja, S., Populationbased incremental learning. A method for integrating genetic search based function optimization and competitive learning (1994) DTIC Document; Baluja, S., Caruana, R., Removing the genetics from the standard genetic algorithm (1995) The International Conference on Machine Learning 1995, pp. 3846; Harik, G.R., The compact genetic algorithm (1999) Evolutionary Computation, IEEE Transactions on, 3, pp. 287297; Wei, W.W.S., (1990) Time Series Analysis, , AddisonWesley Redwood City, California; Hussain, B., AlDabbagh, R., A canonical genetic algorithm for likelihood estimator of first order moving average model parameter (2007) Neural Network World, 17, p. 271; AlSarray, B.A.H., Variants of Hybrid Genetic Algorithms for Optimizing Likelihood ARMA Model Function and Many of Problems (2011) Evolutionary Algorithms, pp. 219246; Droste, S., A rigorous analysis of the compact genetic algorithm for linear functions (2006) Natural Computing, 5 (3), pp. 257283. , DOI 10.1007/s1104700690010; Myung, I.J., Tutorial on maximum likelihood estimation (2003) Journal of Mathematical Psychology, 47, pp. 90100; Box, G.E.P., Jenkins, G.M., (1994) Time Series Analysis: Forecasting and Control, , Prentice Hall PTR; Chrysoula, D.F., YuleWalker Estimation for the MovingAverage Model (2011) International Journal of Stochastic Analysis, 2011; Aporntewan, C., Chongstitvatana, P., A hardware implementation of the Compact Genetic Algorithm Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 2001, 1, pp. 624629; Spyros, M., (1997) Forecasting: Methods and Applications, , ed: New York, USA: John Wiley & Sons Inc; Tsutsui, S., Probabilistic modelbuilding genetic algorithms in permutation representation domain using edge histogram (2002) Parallel Problem Solving from Nature  PPSN VII, pp. 224233 
Uncontrolled Keywords:  Canonical Genetic Algorithm (CGA), compact Genetic Algorithm (cGA), Likelihood Function, Mean Square Error (MSE), Moment Estimation Method, Moving Average (MA), Compact genetic algorithm, Likelihood functions, Moving averages, Communication, Estimation, Information technology, Maximum likelihood estimation, Mean square error, Optimization, Genetic algorithms. 
Subjects:  T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering 
Divisions:  Faculty of Engineering 
Depositing User:  Mr Jenal S 
Date Deposited:  07 Feb 2013 01:24 
Last Modified:  25 Oct 2019 04:02 
URI:  http://eprints.um.edu.my/id/eprint/4731 
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