초록
Genetic algorithm (GA), which has a powerful searching ability and is comparatively easy to use and also to apply, is in the spotlight in the field of the optimization for mechanical systems these days. However, it also contains some problems of slow convergence and low efficiency caused by a huge amount of repetitive computation. To improve the processing efficiency of repetitive computation, some papers have proposed paralleled GA these days. There are some cases that mention the use of gray code or suggest using gray code partially in GA to raise its slow convergence. Gray code is an encoding of numbers so that adjacent numbers have a single digit differing by 1. A binary gray code with n digits corresponds to a hamiltonian path on an n-dimensional hypercube (including direction reversals). The term gray code is open used to refer to a reflected code, or more specifically still, the binary reflected gray code. However, according to proposed reports, gray code GA has lower convergence about 10-20% comparing with binary code GA without presenting any results. This study proposes new Full gray code GA (FGGA) applying a gray code throughout all basic operation fields of GA, which has a good data processing ability to improve the slow convergence of binary code GA.