Fitness Agnostic Adaptive Sampling Lexicase Selection

Jared Moore, Adam Stanton

Research output: Chapter in Book/Published conference outputConference publication


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
Original languageEnglish
Title of host publicationProceedings of the 2023 Conference on Artificial Life
Number of pages8
Publication statusE-pub ahead of print - 24 Jul 2023
EventALIFE 2023: Ghost in the Machine: The 2023 Conference on Artificial Life - Sapporo, Japan
Duration: 24 Jul 202328 Jul 2023

Publication series

NameArtificial Life Conference Proceedings
PublisherMIT Press


ConferenceALIFE 2023: Ghost in the Machine
Abbreviated titleALIFE 2023
Internet address

Bibliographical note

© 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


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