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

Jared M. Moore; Adam Stanton

doi:10.1162/isal_a_050

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

Jared M. Moore, Adam Stanton

Computer Science

Research output: Chapter in Book/Published conference output › Conference 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 language	English
Title of host publication	Proceedings of ECAL 2017
Editors	Carole Knibbe, others
Pages	290-297
DOIs	https://doi.org/10.1162/isal_a_050
Publication status	Published - 1 Sept 2017
Event	ECAL 2017, the Fourteenth European Conference on Artificial Life - Lyons, France Duration: 4 Sept 2017 → 8 Sept 2017

Conference

Conference	ECAL 2017, the Fourteenth European Conference on Artificial Life
Abbreviated title	ECAL 2017
Country/Territory	France
City	Lyons
Period	4/09/17 → 8/09/17

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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10.1162/isal_a_050

isal_a_050
©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/
Final published version, 1.5 MBLicence: CC BY-NC-ND 4.0

Cite this

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title = "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. ",

author = "Moore, {Jared M.} and Adam Stanton",

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PY - 2017/9/1

Y1 - 2017/9/1

N2 - 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.

AB - 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.

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ER -

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

Abstract

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