THE SOLUTION OF PROBLEM OF PARAMETERIZATION OF THE PROXIMITY FUNCTION IN ACE USING GENETIC ALGORITHM

Khamroev Alisher

Abstract: In this work, a new approach for defining the value of the proximity function, which is carried out in the second step of the Algorithms for Calculating Estimates (ACE) in the area of Pattern Recognition, is presented. The value of the proximity function is defined as a part of corresponding features of two objects. The main attention is paid to essential features of the polytypic in a given training set. One of the important problems of the ACE is to compare the values of fuzzy attributes. The main idea of this approach is considering the proximity the corresponding quantitative and qualitative features together. Here a complexity of comparing the qualitative features and an approach of overcoming such complexity are considered. Such features include the features with fuzzy values. The membership function of fuzzy set theory is used for determining membership degrees of the feature values describing with linguistic values for improve the quality of ACE. The steps of the algorithm for transfer the results is obtained from the comparison of the two values of fuzzy feature by using membership function to the proximity function. The membership function with two parameters (b and c) is used. For defining optimal values of these parameters evolutionary algorithms for solving optimization problems are used, one of them is Genetic algorithm. By using genetic algorithm initial parameters’ values of the membership function are generated and transmitted to the proximity function. The ACE is run and value of functional quality is defined during the training process with given training set. If the value of the functional quality is not sufficiently high than the values obtained by Genetic algorithm, these values are regenerated using special operators (selection, crossover, mutation) of the Genetic algorithm. The algorithm for selection optimal values of the parameters of the membership function using the Genetic algorithm is given.

Keywords: ACE, proximity function, Genetic algorithm, membership function, parameters, operators

DOI: https://doi.org/10.15623/ijret.2015.0412020