An adaptive Parameters Binary-Real Coded Genetic Algorithm for Real Parameter Optimization: Performance Analysis and Estimation of Optimal Control Parameters

Genetic algorithms (GAs) are vital members within the family biologically inspired algorithms. It has been proven that the performance of GAs is largely affected by the type of encoding schemes used to encode optimization problems. Binary and real encoding schemes are the most popular ones. However, it is still controversial to decide the superiority of one of them for GAs performance. Therefore, we have recently proposed binary-real coded GA (BRGA) that has the ability to use both encoding schemes at the same time. BRGA relies on a parameterized hybrid scheme to share the computational power and coordinate the cooperation between binary coded GA (BGA) and real coded GA (RGA). In this article, we use CEC’2005 benchmark suite of 25 functions to analyze quality and time performance of BRGA and in comparison with original binary and real coded component GAs. To demonstrate the performance of BRGA, we compare it with the performance of some other EAs from the literature. In addition, we implement a robust parameter tuning procedure that relies on techniques from statistical testing, design of experiments and Response Surface Methodology (RSM) to estimate the optimal values for control parameters that can secure a good performance for BRGA against specific problems at hand.