Work

Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE)

Year: 2022

Type: preprint

Abstract: 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 interpretability). We investigate herein whether multiple objectives can be dynamically tuned by our recently proposed coevolutionary algorithm, SAFE (Solution And Fitness Evolution). We find that SAFE is able to automatically tune accuracy and complexity with no performance loss, as compared with a standard evolutionary algorithm, over complex simulated genetics datasets produced by the GAMETES tool.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2206.15409

Authors: Sipper, Moshe, Moore, Jason H., Urbanowicz, Ryan J.

Institutions: Ben-Gurion University of the Negev, University of Pennsylvania

Language: English

Topic: Evolutionary Algorithms and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Institutes of Health

Awards: G9000222778, G9000325014

OpenAlex ID: W4283773216

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