When Specialists Transition to Generalists: Evolutionary Pressure in Lexicase Selection

Jared M. Moore, Adam Stanton

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Generalized behavior is a long standing goal for evolutionary robotics. Behaviors for a given task should be robust to perturbation and capable of operating across a variety of environments. We have previously shown that Lexicase selection evolves high-performing individuals in a semi-generalized wall crossing task–i.e., where the task is broadly the same, but there is variation between individual instances. Further work has identified effective parameter values for Lexicase selection in this domain but other factors affecting and explaining performance remain to be identified. In this paper, we expand our prior investigations, examining populations over evolutionary time exploring other factors that might lead to generalized behavior. Results show that genomic clusters do not correspond to performance, indicating that clusters of specialists do not form within the population. While early individuals gain a foothold in the selection process by specializing on a few wall heights, successful populations are ultimately pressured towards generalized behavior. Finally, we find that this transition from specialists to generalists also leads to an increase in tiebreaks, a mechanism in Lexicase, during selection providing a metric to assess the performance of individual replicates.
Original languageEnglish
Title of host publicationThe 2020 Conference on Artificial Life
Pages719-726
DOIs
Publication statusPublished - 1 Jul 2020
Event2020 Conference on Artificial Life -
Duration: 13 Jul 202018 Jul 2020
https://www.robot100.cz/alife2021/

Conference

Conference2020 Conference on Artificial Life
Abbreviated titleALIFE 2020
Period13/07/2018/07/20
Internet address

Bibliographical note

© 2020 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

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