An approach based on Go knowledge to calculate the static evaluation value is proposed, and the parameter of the model is further optimized by genetic algorithm. Through this approach, various static evaluation models based on different levels can be obtained. The results of text show that, compared with the original model, the accuracy and operation speed of the proposed approach are promoted by 93% and 35~ respectively. We expect that, with such operation capacity, this model can be applied in the module of the middle game and end game of computer Go. This model has a practical utilization in researches on computer games, artificial intelligence and game software.%提出了一种计算静态评估值的数学模型,并结合遗传算法对模型参数进行了优化。利用此方法可以获得不同棋力下分别对应的不同静态评估算法模型。对比实验结果表明,该模型能将运算精度提升93%,运算速度提升35%;其运行能力可以应用于计算机围棋中盘、收官等模块中,对计算机博弈、人工智能以及游戏软件的研究具有重要意义。
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