APPLICATION OF GENETIC PROGRAMMING FOR DECISION OF SIMBOLIC REGRESSION PROBLEM
There is considered the decision of symbolic regression problem by means of genetic programming method. The problem input data include the set of independent variables and constants (arguments of function), the set of elementary functions, list of fitness-cases (table containing reference values of unknown function at different values of the arguments). Result of the problem decision is mathematical expression in symbolic form, which fits the functional dependence, described by fitness-cases, in the best way. There are described the problem and the method of decision, the way of alternative decisions representation and quality measuring, algorithm of genetic search, modifications of standard genetic operators, parameters of search algorithm. The result of running a program, which realizes described method, is considered.