An Efficiency of Third Party Genetic Algorithms Software Libraries in Mobile Distributed Computing for Financial Time Series Forecasting

Genetic algorithms are very well-known optimization meta-heuristics. They are very well presented in mathematical applications like Matlab, R, and others. Such specific implementations are not proper for industrial software development. Because of its popularity, genetic algorithms have become implemented as third-party software libraries. The popularity of Android OS with its capabilities for running Java source code attracted the usage of external software libraries for achieving mobile distributed computing tasks. Training of artificial neural networks and curve fitting by the usage of genetic algorithms brought financial time series forecasting to the mobile world. In this study, two of the most popular genetic algorithm software libraries are compared in order to be used in mobile distributed computing application.