Performance Comparison Between Gray Coded and Binary Coded Genetic Algorithms for Inverse Shape Optimization of Magnetic Devicec

Genetic Algorithms (GAS) work with coded information rather than directly with the physical values of the optimized variables, therefore, they are very robust and easy applicable as searching and optimization tools. The coding method, however, is usually not general and mainly depends of the analysis problem. In this paper, we show that the coding method has additionally large influence on the computation speed and the accuracy of the obtained results. We present a comparison between Gray coded and binary coded GAs for inverse shape optimization of a rotating machine pole face. We show that the Gray coded GA is better suited for inverse optimization and could provide more accurate results for shorter computation time