Fitness Agnostic Adaptive Sampling Lexicase Selection


Lexicase selection is an effective many-objective evolutionary algorithm across many problem domains. Lexicase can be computationally expensive, especially in areas like evolutionary robotics where individual objectives might require their own physics simulation. Improving the efficiency of Lexicase selection can reduce the total number of evaluations thereby lowering computational overhead. Here, we introduce a fitness agnostic adaptive objective sampling algorithm using the filtering efficacy of objectives to adjust their frequency of occurrence as a selector. In a set of binary genome maximization tasks modeled to emulate evolutionary robotics situations, we show that performance can be maintained while computational efficiency increases as compared to ϵ-Lexicase

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Divisions: College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Applied AI & Robotics
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
Additional Information: © 2023 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit
Event Title: ALIFE 2023: Ghost in the Machine
Event Type: Other
Event Dates: 2023-07-24 - 2023-07-28
Last Modified: 22 Apr 2024 07:36
Date Deposited: 23 Jun 2023 14:52
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Related URLs: https://direct. ... al/35/17/116898 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2023-07-24
Published Online Date: 2023-07-24
Accepted Date: 2023-05-01
Authors: Moore, Jared
Stanton, Adam



Version: Accepted Version

Access Restriction: Restricted to Repository staff only


Version: Published Version

License: Creative Commons Attribution

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