Using Genetic Algorithms for Solving the Comparison-Based Identification Problem of Multifactor Estimation Model
- 1 Computer Engineering Department, University of Jordan, Amman, Jordan
- 2 Computer Science Department, Kharkov National University, Kharkov, Ukraine
- 3 Computer Science Department, Kharkov National University, Kharkov, Ukraine
Abstract
In this paper the statement and the methods for solving the comparison-based structure-parametric identification prob lem of multifactor estimation model are addressed. A new method that combines heuristics methods with genetic algo rithms 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 char acteristics degrees and all their possible combinations is proposed in this paper. The use of nonlinear methods for iden tification 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 consider able saving in time and accuracy performance. This method is also simpler and more evident for the decision maker (DM) than other methods .
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