Nonlinear mapping using real-valued genetic algorithm

A real-valued genetic algorithm (RGA) for the optimization problem of real-valued parameters was developed. Two special operators, orientated mutation and immigration, were introduced to enhance the efficiency of the searching process and confront the causes of premature convergence. The proposed RGA was applied to complicated nonlinear mapping problems. Experimental results show that the proposed RGA can alleviate the problem of premature convergence and successfully find acceptable optima with high efficiency.