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

Jared M. Moore, Adam Stanton

Research output: Chapter in Book/Published conference outputConference publication

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.
Original languageEnglish
Title of host publicationProceedings of ECAL 2017
EditorsCarole Knibbe, others
Pages290-297
DOIs
Publication statusPublished - 1 Sept 2017
EventECAL 2017, the Fourteenth European Conference on Artificial Life - Lyons, France
Duration: 4 Sept 20178 Sept 2017

Conference

ConferenceECAL 2017, the Fourteenth European Conference on Artificial Life
Abbreviated titleECAL 2017
Country/TerritoryFrance
CityLyons
Period4/09/178/09/17

Bibliographical note

©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/

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