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A novel genetic algorithm, self-adaptation genetic algorithm based on knowledge (SAKGA) is presented. The key thought of the algorithm lies in that the length of variable binary encoding string is shortened using code table, and operators are manipulated towards the promising direction using knowledge. The use of code table, extraction and culture of eminent knowledge-based genes are dealt with in this paper. The optimization data show that SAKGA can produce performance improvement in execution time and accuracy, and the proposed algorithm has potential to solve engineering optimization problems.