Optimizing the Parameters of A Physical Exercise Dose-Response Model: An Algorithmic Comparison

TL;DR

This study compares local and global optimization algorithms for fitting non-linear dose-response models in exercise physiology, finding evolutionary algorithms more robust and effective than traditional local methods.

Contribution

It introduces and evaluates an evolutionary computation algorithm as a superior alternative for parameter fitting in non-linear dose-response models.

Findings

Evolutionary algorithms outperform local algorithms in model fit.

Global optimization provides more consistent results.

Evolutionary methods are faster and more robust.

Abstract

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.