Avoiding Genetic Algorithm Permutation Convergence Using Baker’s Map

Abstract

 Non-linear optimization problems are one of the most widespread in the world many which needed to solve. There are several algorithms found to solve such problems one of these is Genetic Algorithm (GA). This algorithm is mainly based on randomness in its process to determine the optimal solution, but it has drawbacks one of these is increasing the number of iterations for reaching the global optimum which is called (permuting convergence). The Baker’s Map (BM) is used in this paper to overcome the drawback of GA and to increase their performance. The experimental results of the proposed method show their ability to reach optimal solutions with fewer iterations and significantly improve the basic GA's solution quality.