中文摘要：

提出了一种改进单目标自适应遗传算法（MSAGA）。针对自适应搜索遗传算法（ASNSGA）遗传代数设置不合理与单目标非支配排序自适应遗传算法（SONSAGA）因非均匀种群而引起拟合新误差的缺陷,MSAGA算法通过临界遗传代数与变量取值区间的自适应调整,同时提高了计算精度与计算速度。将MSAGA算法应用于车削优化,实例结果显示不仅优于标准遗传算法（GA）与SONSAGA算法的优选值,而且计算速度比SONSAGA算法提高了75.9669%。结果证明MSAGA算法用于车削用量参数的优化是有效的。MSAGA算法能快速自适应获得满足给定精度的变量优选值,为车削优化提出了新思路。

英文摘要：

A modified single-object adaptive genetic algorithm（ MSAGA） is developed for the optimization of turning parameters. The MSAGA overcomes the defects the unreasonable setting generation in the adaptive search nondominated sorting genetic algorithm（ ASNSGA） and the new fitting error based on non-uniform population of the adaptive the signal objective non-dominated sorting adaptive genetic algorithm（ SONSAGA）. By adjusting automatically critical generation and variable value interval,the MSAGA improves the computing accuracy and speed. The MSAGA simulations for the optimization of turning show that the results obtained are better than those of the standard genetic algorithm（ GA） and SONSAGA. The computing speed of the MSAGA improves 75. 9669% than that of the SONSAGA and the MSAGA can rapidly acquire the turning parameters optimization values. Hence the MSAGA provides a novel method for the and adaptively optimization of turning parameters.