Using Genetic Algorithms for Solving the Comparison-Based Identification Problem of Multifactor Estimation Model

Andraws Swidan, Shmatkov Sergey, Bulavin Dmitry

Computer Engineering Department, University of Jordan, Amman, Jordan.
Computer Science Department, Kharkov National University, Kharkov, Ukraine.

DOI: 10.4236/jsea.2013.67044                

Abstract

In this paper the statement and the methods for solving the comparison-based structure-parametric identification problem of multifactor estimation model are addressed. A new method that combines heuristics methods with genetic algorithms is proposed to solve the problem. In order to overcome some disadvantages of using the classical utility functions, the use of nonlinear Kolmogorov-Gabor polynomial, which contains in its composition the first as well as higher characteristics degrees and all their possible combinations is proposed in this paper. The use of nonlinear methods for identification of the multifactor estimation model showed that the use of this new technique, using as a utility function the nonlinear Kolmogorov-Gabor polynomial and the use of genetic algorithms to calculate the weights, gives a considerable saving in time and accuracy performance. This method is also simpler and more evident for the decision maker (DM) than other methods.

Keywords

Genetic Algorithm; Comparatory Identification; Fitness-Function; Chromosome; Crossover; Mutation

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	A. O. Ovezgeldyev and K. E. Petrov, “Comparatory Identification of Linear Multifactor Estimation Models Parameters,” Radioelektronika i Informatika, Vol. 2, No. 3, 1998, pp. 41-43.
[2]	A. O. Ovezgeldyev, E. G. Petrov and K. E. Petrov, “Synthesis and Identification of Multifactor Estimation and Optimization Models,” Naukova Dumka, Kiev, 2002, 161 p.
[3]	G. K. Voronovsky, К. V. Machotilo, S. N. Petroshev and S. А. Sergeev, “Genetic Algorithms, Artificial Neural Networks and Virtual Reality Problems,” Osnova, Kharkov, 1997, 112 p.
[4]	V. М. Kureychik, “Genetic Algorithms. State. Problems,” Teoriya i Systemy Upravleniya, No. 1, 1999, pp. 144-160.
[5]	E. G. Petrov and Д. А. Булавин, “Application of Chebyshev’s Dot and Genetic Algoritms’ Methods for Determination the Structure of Multifactor Estimation Model,” Problemy Bioniki, No. 58, 2003, pp. 36-44.
[6]	E. G. Petrov and Д. А. Булавин, “Application of Genetic Algoritms’ Method for Solving the Multifactor Estimation Model Comparatory Identification Problem,” Radioelektronika i Informatika, No. 1, 2003, pp. 89-93.
[7]	J. Zhang, H. Chung and W. L. Lo, “Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms,” IEEE Transactions on Evolutionary Computation, Vol. 11, No. 3, 2007, pp. 326-335. doi10.1109/TEVC.2006.880727
[8]	L. M. Schmitt, “Theory of Genetic Algorithms II: Models for Genetic Operators over the String-Tensor Representation of Populations and Convergence to Global Optima for Arbitrary Fitness Function under Scaling,” Theoretical Computer Science, Vol. 310, No. 1, 2004, pp. 181-231. doi10.1016/S0304-3975(03)00393-1
[9]	D. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning,” Addison-Wesley Professional, Reading, 1989.
[10]	А. P. Rotshteyn, “Intelligent Identification Technologies,” Universum-Vinnica, Vinnica, 1999, 320 p.