1·Using BP neural networks, the fitness of GA was calculated, on the basis of which the mechanical structure was optimized.
用神经网络计算遗传算法的适应度，对结构进行优化。
2·At last we use a simple example to show the process of test data of basic type generation with GA.
最后用一个简单的实例说明了使用遗传算法生成基本数据类型测试数据的过程。
3·Calculation examples show that the optimization effect of the improved algorithm is better than that of the traditional GA.
实例计算表明，其优化效果优于传统遗传算法。
4·GA is an adaptive and global optimizing probability search method. It directly USES the objective function as search message.
遗传算法是一种自适应全局优化概率搜索算法，它直接以目标函数作为搜索信息。
5·The thesis makes research on the knowledge representation of GA composing.
论文对遗传算法作曲中的知识表示进行了研究。