  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.
