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

• 作者: Andraws Swidan Bulavin Dmitry Shmatkov Sergey
• 来源期刊: 软件工程与应用(英文)
• 学科: 医学
• 关键词: Genetic Algorithm Comparatory Identification Fitness-Function CHROMOSOME CROSSOVER MUTATION
• 年，卷（期）: rjgcyyyyw_2013,（7）
• 页码范围: 349-353
• 页数: 5页
• 分类号: R73

摘要

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.