145 citations · 793 across the 36 of their papers we have counts for
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Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
When seeking a predictive model in biomedical data, one often has more than a single objective in mind, e.g., attaining both high accuracy and low complexity (to promote interpreta…
Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
We have recently presented SAFE -- Solution And Fitness Evolution -- a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate…
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
We recently highlighted a fundamental problem recognized to confound algorithmic optimization, namely, \textit{conflating} the objective with the objective function. Even when the…
Symbolic-Regression Boosting
Moshe Sipper, Jason H Moore
Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regres…