32 citations · 87 across the 13 of their papers we have counts for
15 papers
Coevolving Artistic Images Using OMNIREP
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
We have recently developed OMNIREP, a coevolutionary algorithm to discover both a representation and an interpreter that solve a particular problem of interest. Herein, we demonstr…
New Pathways in Coevolutionary Computation
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
The simultaneous evolution of two or more species with coupled fitness -- coevolution -- has been put to good use in the field of evolutionary computation. Herein, we present two n…
STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers
Ryan J. Urbanowicz, Harsh Bandhey, Brendan T. Keenan +19
While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbias…
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…