Adaptive Genetic Algorithm Renewed by Migration Operator

Abstract

In the present study, the Genetic Algorithm and the developed Adaptive Genetic Algorithm are used to solve optimization problems faced in modeling of complex systems. While using the Genetic Algorithm and the Adaptive Genetic Algorithm, the most important problem encountered is that these algorithms are prone to getting stuck in local bests. For example, in the complex system data optimization process using the Genetic Algorithm, it has been observed that it is frequently fitted to local bests in a certain period of time. The reasons are that, many mutations cannot be performed in the limited number of iterations, and because the number of individuals is limited, the population is filled with the same set of solutions in a short time. Therefore, the migration operator has been added to the Genetic Algorithm and the Adaptive Genetic algorithm in order to avoid local bests and to provide that these algorithms search in a wider area of the large search space of complex systems. In the present study, we have tested these algorithms using the migration operator in the Lotka Volterra Model. When the results are examined, it is observed that the Adaptive Genetic Algorithm with migration operator outperforms the Genetic Algorithm and the targeted success is achieved in complex system optimization. In the rest of the paper, the algorithms used in the Method Section are explained with outlines. In the Experimental Studies Section, the different algorithms are tested and compared with each other in the Lotka-Voltera model and numerical test functions. In the Conclusions and Discussions Section, brief information is given about general results and future studies.