Objective Sampling Strategies for Generalized Locomotion Behavior with Lexicase Selection


Controllers capable of exhibiting multiple behaviors is a longstanding goal in artificial life. Evolutionary robotics approaches have demonstrated effective optimization of robotic controllers, realizing single behaviors in a variety of domains. However, evolving multiple behaviors in one controller remains an outstanding challenge. Many objective selection algorithms are a potential solution as they are capable of optimizing across tens or hundreds of objectives. In this study, we use Lexicase selection evolving animats capable of both wall crossing and turn/seek behaviors. Our investigation focuses on the objective sampling strategy during selection to balance performance across the two primary tasks. Results show that the sampling strategy does not significantly alter performance, but the number of evaluations required varies significantly across strategies.

Publication DOI: https://doi.org/10.1162/isal_a_00398
Additional Information: Copyright © 2021 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
Last Modified: 27 Dec 2023 10:12
Date Deposited: 20 Jun 2023 13:35
Full Text Link:
Related URLs: https://direct. ... 21/33/73/102892 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2021-07-19
Published Online Date: 2021-07-19
Authors: Moore, Jared M.
Stanton, Adam



Version: Published Version

License: Creative Commons Attribution

| Preview

Export / Share Citation


Additional statistics for this record