"测量装置制造中的标定点选择问题：基于遗传算法的优化方案研究"

Summary The calibration of measuring devices is a crucial step in the manufacturing process, as it directly impacts production costs and product quality. This report, authored by Zhou Ruiwen, addresses the issue of selecting calibration points for a measuring device and transforms it into a combinatorial optimization problem. It introduces a solution framework based on a genetic algorithm with adaptive improvement, and conducts extensive experiments using a dataset of temperature measuring devices with significant nonlinearity and individual differences. The results obtained from the experiments highlight the importance of optimal calibration schemes and various adaptive improvement measures. Additionally, the report explores the impact of different fitting interpolation methods through numerical simulations, analyzing and discussing their effects on calibration errors and cost considerations. The key findings of this study emphasize the significance of the genetic algorithm, curve fitting interpolation, combinatorial optimization, device calibration, and error analysis.