A variable-based genetic algorithm
Kuang-Tsang Jean, Yung-Yaw Chen · 2002
Genetic algorithms are very powerful search algorithms based on the mechanics of natural selection and natural genetics. As well known, one of differences from many other conventional search algorithms is that genetic algorithms require the natural parameter set of the optimization problem to be coded as a finite-length string. However, the encoding and decoding processes waste many computation time and lose the accuracy of the parameters. In this paper, a novel variable-based genetic algorithm is proposed. The algorithm processes the parameters themselves without coding. It can save the coding processing time and get more accurate values of the parameters. Finally, the system identification problem has been used to demonstrate the power of the algorithm.>