Lexicase Selection Outperforms Previous Strategies for Incremental Evolution of Virtual Creature Controllers

Abstract

Evolving robust behaviors for robots has proven to be a challenging problem. Determining how to optimize behavior for a specific instance, while also realizing behaviors that generalize to variations on the problem often requires highly customized algorithms and problem-specific tuning of the evolutionary platform. Algorithms that can realize robust, generalized behavior without this customization are therefore highly desirable. In this paper, we examine the Lexicase selection algorithm as a possible general algorithm for a wall crossing robot task. Previous work has resulted in specialized strategies to evolve robust behaviors for this task. Here, we show that Lexicase selection is not only competitive with these strategies but after parameter tuning, actually exceeds the performance of the specialized algorithms.

Publication DOI: https://doi.org/10.1162/isal_a_050
Additional Information: ©2017 Massachusetts Institute of Technology. This work is licensed to the public under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 license (international): https://creativecommons.org/licenses/by-nc-nd/4.0/
Event Title: ECAL 2017, the Fourteenth European Conference on Artificial Life
Event Type: Other
Event Dates: 2017-09-04 - 2017-09-08
ISBN: 978-0-262-34633-7
Last Modified: 20 May 2024 07:49
Date Deposited: 12 Sep 2022 16:58
Full Text Link:
Related URLs: https://machine ... n-ecal-2017.pdf (Author URL)
https://direct. ... l2017/290/99553 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2017-09-01
Authors: Moore, Jared M.
Stanton, Adam

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