  In many global Optimization Problems, it is required to evaluate a global
point (min or max) in large space that calculation effort is very high. In this
paper is presented new approach for optimization problem with subdivision
labeling method (SLM) but in this method for higher dimensional has high
computational. SLM Genetic Algorithm (SLMGA) in optimization problems is one of
the solutions of this problem. In proposed algorithm the initial population is
crossing points and subdividing in each step is according to mutation. RSLMGA
is compared with other well known algorithms: DE, PGA, Grefensstette and
Eshelman and numerical results show that RSLMGA achieve global optimal point
with more decision by smaller generations.
