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 MasterSlave Model, the OnePopulation FineGrained Model, the MultiPopulation CoarseGrained Model, and the Hierarchical Model. The practical part deals with the design and implementation of parallelized GA.

RealCoded Genetic Algorithm Realizing Fast Convergence by Reducing Its Population Size Just generation gap (JGG) is a generational alternation model of realcoded genetic algorithms (GAs), and is excellent at finding the global optimum solution of a function optimization problem. However, its population size is large, and therefore its convergence speed is low. A method to accelerate the convergence speed is to reduce the population size. However, if it is reduced throughout the search by JGG, the population diversity is lost, which may cause the failure to find the global optimum solution. The population size should be reduced during only a part of the search period during which the population diversity is not lost. In this paper, we propose a realcoded GA realizing fast convergence by introducing the reduction of the population size into JGG. In the proposed method, the population size is reduced during only an early or late search period. The performance of the proposed method is empirically evaluated by comparing it with only JGG and an existing GA.