  Optimization problem, nowadays, have more application in all major but they
have problem in computation. Calculation of the optimum point in the spaces
with the above dimensions is very time consuming. In this paper, there is
presented a new approach for the optimization of continuous functions with
rotational mutation that is called RM. The proposed algorithm starts from the
point which has best fitness value by elitism mechanism. Then, method of
rotational mutation is used to reach optimal point. In this paper, RM algorithm
is implemented by GA(Briefly RMGA) and is compared with other well- known
algorithms: DE, PGA, Grefensstette and Eshelman [15, 16] and numerical and
simulation results show that RMGA achieve global optimal point with more
decision by smaller generations.
