Optimizing the Parameters of A Physical Exercise Dose-Response Model: An Algorithmic Comparison
This research offers a potentially more robust and faster method for exercise physiologists to fit non-linear dose-response models, addressing limitations of traditional local optimization algorithms.
This research compared a local and a global optimization algorithm for fitting parameters of a non-linear dose-response model in exercise physiology. The evolutionary computation-based algorithm consistently achieved a stronger model fit and holdout performance over 1000 experimental runs compared to the local search algorithm.
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.