Analysis of the Possibilities of the Evolutionary Algorithm to Improve the Neural Model of the TGE S.A. Day Ahead Market System Using Selected Programming Environments

Abstract

The article contains selected research results regarding the analysis of the possibility of using the Evolutionary Algorithm to improve neural models of intelligent systems using selected programming environments. Choosing an appropriate programming language is one of the basic activities in the process of implementing complex algorithms, which include methods of artificial neural networks and evolutionary algorithms. Due to the fact that the object of the researchwas an intelligent Day Ahead Market system operating on the Polish Power Exchange and the modeling methods were artificial neural networks and evolutionary algorithms, it was decided to use very high-level programming languages such as Python, Matlab and C# for implementation and associated development environments. It turned out, among other things, that each of these languages and programming environments has its advantages and disadvantages, but all of them are very useful due to their useful syntax and rich included libraries. A thorough analysis of the implementation shows, among other things, that the choice of programming language affects the efficiency, speed and quality of the obtained implementations of system models. Against this background, the advantages and disadvantages of individual programming languages are shown, especially in the context of implementing evolutionary algorithms. The research results indicate directions for selecting an appropriate programming language and the associated programming environment for system modeling using artificial neural networks and evolutionary algorithms. In addition, the method of analysis, as well as the method of modeling and implementation was shown on the example of a specific system, which was the Day Ahead Market system of TGE
S.A.