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A stochastic genetic algorithm (StGA) technique is presented to deal with global optimization of continuous problems. In this algorithm, a novel coding scheme called "stochastic coding" is employed, so that the search space is explored in terms of stochastic regions towards the near-global solution. The effectiveness and efficiency of the algorithm are demonstrated through performing optimization on several test functions and the results are compared with the well established fast evolutionary programming (FEP) technique in terms of the global optimization accuracy and the computational efficiency. Comparisons show that StGA can improve the accuracy of the optimization results on these functions by up to several orders as compared with FEP, whereas computational effort required by StGA is on average about ten times less than FEP.