Comparison of Models of Parallelized Genetic Algorithms

The aim of the paper is to describe the most widely used methods of parallelization of Genetic Algorithms (GA) and subsequently to use the outputs of the theoretical part for the design of implementation. Python was chosen as the implementation language, so the design is implemented with this language in mind. Selected problems of sequential GA are described in the theoretical part of the paper. Optimization problems and parallel models are described. They are the Global One - Population Master-Slave Model, the One-Population Fine-Grained Model, the Multi-Population Coarse-Grained Model, and the Hierarchical Model. The practical part deals with the design and implementation of parallelized GA.