Abstract: Aiming at problems of genetic algorithm,such as a worse local search,a slower global optimization,and search efficiency depending on the selection of penalty function obviously,a crossover operator which searchs from both feasible and infeasible solution space simultaneously,a mutation operator which can rapidly search prophase and hold global optimal solution anaphase,and a selection operator which can hold optimal solution are designed.Moreover,the reversion and degradation are avoided via using the searched solution space.Based on all these,a novel effective genetic algorithm for global optimization is proposed and its global convergence is proved.At last,the simulation result shows that this algorithm can rapidly find the global extremum point.