A program searching for a functional dependence using genetic programming with coefficient adjustment
Vladimír Hlaváč · 2016
When modeling many traffic problems, it is necessary to find the functional dependence of the output of two input variables. This task can be solved by a neural network, by using some spline interpolation or polynomials, etc. These approaches can produce a model, but its internal description is unreadable and its transfer to another program can be difficult. Therefore, a program to determine this functional dependence using genetic programming has been developed. The result is prepared in such a way that it can be transferred into a source code of another program, or copied to an MS Excel sheet. The program reads data available as triplets, [[x, y], z], and looks for their functional interdependencies by using a selected set of elementary functions and a vector of multiplicative constants. The input data do not have to meet any additional conditions. They can be defined on measured intervals, or even as individual points. For a successful outcome, the only condition is to have a sufficient amount of data. For some functions, the level of noise has to be determined in order to make the model complete. In this case, noise characteristics can be evaluated from the results of the program.