论文标题
优化体育锻炼剂量反应模型的参数：算法比较
Optimizing the Parameters of A Physical Exercise Dose-Response Model: An Algorithmic Comparison
论文作者
论文摘要
这项研究的目的是比较拟合在运动生理领域中使用的常见非线性剂量反应模型的参数的任务时，将局部和全局优化算法的鲁棒性和性能进行比较。传统上，剂量反应模型的参数已使用非线性最小二乘程序与局部优化算法结合使用。但是，这些算法表明了它们在全球最佳解决方案上收敛的能力的局限性。该研究目的是将基于进化计算的算法用作拟合非线性剂量反应模型的替代方法。我们比较超过1000次实验运行的结果表明，基于进化计算的算法的出色性能与本地搜索算法相比，始终如一地实现更强的模型拟合和保持性能。这项最初的研究将表明，在拟合非线性剂量反应模型的参数时，基于全球进化计算的优化算法可能会呈现局部算法的快速且可靠的替代方案。
The purpose of this research was to compare the robustness and performance of a local and global optimization algorithm when given the task of fitting the parameters of a common non-linear dose-response model utilized in the field of exercise physiology. Traditionally the parameters of dose-response models have been fit using a non-linear least-squares procedure in combination with local optimization algorithms. However, these algorithms have demonstrated limitations in their ability to converge on a globally optimal solution. This research purposes the use of an evolutionary computation based algorithm as an alternative method to fit a nonlinear dose-response model. The results of our comparison over 1000 experimental runs demonstrate the superior performance of the evolutionary computation based algorithm to consistently achieve a stronger model fit and holdout performance in comparison to the local search algorithm. This initial research would suggest that global evolutionary computation based optimization algorithms may present a fast and robust alternative to local algorithms when fitting the parameters of non-linear dose-response models.
