中文摘要：

由于进化设计中生成解的不可预测性,人们常常通过人工选择最优解;针对这一问题,提出一种改进的遗传算法——蔓延遗传算法;根据客户需求确定满足约束条件的可行解,通过遗传运算,将属于可行解的个体逐渐在群体中蔓延,使群体中充满满足需求的可行解;再通过关键点的选取方法,结合非均匀有理B样条方法绘制三维图形;从而在进化设计中可以通过适应度选择生成解并且自动对形状进行可视化。实例分析表明这种蔓延遗传算法收敛速度快,具有较好的在线性能与离线性能,能够选择出满足顾客需求的生成解。

英文摘要：

Because the generation solution is unpredictable in evolutionary design,the optimal solution is generally selected by man.An improved genetic algorithm called spreading genetic algorithm is proposed.This approach can solve the problem through selecting the optimal solution by fitness.The feasible solutions under constraint conditions are confirmed by the customer requirements.The individuals belonging to the feasible solutions are gradually spread in the colony through the operation of genetic algorithm,so that the colony is full of feasible solutions that meet the requirements.Then 3D graphs are drawn by using key points selection method and NURBS technique.Thus in the evolutionary design the generation solution can be selected through fitness,and shape visualization is automatically carried out.An example analysis shows that the spreading genetic algorithm has high convergence speed,good on-line and off-line performances.