ITU

Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Standard

Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding. / Veenstra, Frank; Faina, Andres; Risi, Sebastian; Støy, Kasper.

Applications of Evolutionary Computation: 20th European Conference, EvoApplications 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Part I. Springer, 2017. (Lecture Notes in Computer Science, Vol. 10199).

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Harvard

Veenstra, F, Faina, A, Risi, S & Støy, K 2017, Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding. in Applications of Evolutionary Computation: 20th European Conference, EvoApplications 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Part I. Springer, Lecture Notes in Computer Science, vol. 10199, Evostar 2017, Amsterdam, Netherlands, 19/04/2017. https://doi.org/10.1007/978-3-319-55849-3_56

APA

Veenstra, F., Faina, A., Risi, S., & Støy, K. (2017). Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding. In Applications of Evolutionary Computation: 20th European Conference, EvoApplications 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Part I Springer. Lecture Notes in Computer Science Vol. 10199 https://doi.org/10.1007/978-3-319-55849-3_56

Vancouver

Veenstra F, Faina A, Risi S, Støy K. Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding. In Applications of Evolutionary Computation: 20th European Conference, EvoApplications 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Part I. Springer. 2017. (Lecture Notes in Computer Science, Vol. 10199). https://doi.org/10.1007/978-3-319-55849-3_56

Author

Veenstra, Frank ; Faina, Andres ; Risi, Sebastian ; Støy, Kasper. / Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding. Applications of Evolutionary Computation: 20th European Conference, EvoApplications 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Part I. Springer, 2017. (Lecture Notes in Computer Science, Vol. 10199).

Bibtex

@inproceedings{3e05d80f13ca438f8722697a1beda810,
title = "Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding",
abstract = "Modular robots oer an important benet in evolutionaryrobotics, which is to quickly evaluate evolved morphologies and controlsystems in reality. However, articial evolution of simulated modularrobotics is a dicult and time consuming task requiring signicant computationalpower. While articial evolution in virtual creatures has madeuse of powerful generative encodings, here we investigate how a generativeencoding and direct encoding compare for the evolution of locomotionin modular robots when the number of robotic modules changes.Simulating less modules would decrease the size of the genome of a directencoding while the size of the genome of the implemented generativeencoding stays the same. We found that the generative encoding is signicantly more ecient in creating robot phenotypes in the initial stagesof evolution when simulating a maximum of 5, 10, and 20 modules. Thisnot only conrms that generative encodings lead to decent performancemore quickly, but also that when simulating just a few modules a generativeencoding is more powerful than a direct encoding for creatingrobotic structures. Over longer evolutionary time, the dierence betweenthe encodings no longer becomes statistically signicant. This leads us tospeculate that a combined approach { starting with a generative encodingand later implementing a direct encoding { can lead to more ecientevolved designs.",
keywords = "Modular Robots, Evolutionary Algorithms, Direct & Generative Encodings",
author = "Frank Veenstra and Andres Faina and Sebastian Risi and Kasper St{\o}y",
year = "2017",
month = jan,
day = "25",
doi = "10.1007/978-3-319-55849-3_56",
language = "English",
isbn = "978-3-319-55848-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
booktitle = "Applications of Evolutionary Computation",
address = "Germany",
note = "Evostar 2017 : The Leading European Event on Bio-Inspired Computation, EVO* ; Conference date: 19-04-2017 Through 21-04-2017",
url = "http://www.evostar.org/2017/",

}

RIS

TY - GEN

T1 - Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding

AU - Veenstra, Frank

AU - Faina, Andres

AU - Risi, Sebastian

AU - Støy, Kasper

PY - 2017/1/25

Y1 - 2017/1/25

N2 - Modular robots oer an important benet in evolutionaryrobotics, which is to quickly evaluate evolved morphologies and controlsystems in reality. However, articial evolution of simulated modularrobotics is a dicult and time consuming task requiring signicant computationalpower. While articial evolution in virtual creatures has madeuse of powerful generative encodings, here we investigate how a generativeencoding and direct encoding compare for the evolution of locomotionin modular robots when the number of robotic modules changes.Simulating less modules would decrease the size of the genome of a directencoding while the size of the genome of the implemented generativeencoding stays the same. We found that the generative encoding is signicantly more ecient in creating robot phenotypes in the initial stagesof evolution when simulating a maximum of 5, 10, and 20 modules. Thisnot only conrms that generative encodings lead to decent performancemore quickly, but also that when simulating just a few modules a generativeencoding is more powerful than a direct encoding for creatingrobotic structures. Over longer evolutionary time, the dierence betweenthe encodings no longer becomes statistically signicant. This leads us tospeculate that a combined approach { starting with a generative encodingand later implementing a direct encoding { can lead to more ecientevolved designs.

AB - Modular robots oer an important benet in evolutionaryrobotics, which is to quickly evaluate evolved morphologies and controlsystems in reality. However, articial evolution of simulated modularrobotics is a dicult and time consuming task requiring signicant computationalpower. While articial evolution in virtual creatures has madeuse of powerful generative encodings, here we investigate how a generativeencoding and direct encoding compare for the evolution of locomotionin modular robots when the number of robotic modules changes.Simulating less modules would decrease the size of the genome of a directencoding while the size of the genome of the implemented generativeencoding stays the same. We found that the generative encoding is signicantly more ecient in creating robot phenotypes in the initial stagesof evolution when simulating a maximum of 5, 10, and 20 modules. Thisnot only conrms that generative encodings lead to decent performancemore quickly, but also that when simulating just a few modules a generativeencoding is more powerful than a direct encoding for creatingrobotic structures. Over longer evolutionary time, the dierence betweenthe encodings no longer becomes statistically signicant. This leads us tospeculate that a combined approach { starting with a generative encodingand later implementing a direct encoding { can lead to more ecientevolved designs.

KW - Modular Robots

KW - Evolutionary Algorithms

KW - Direct & Generative Encodings

UR - https://www.youtube.com/watch?v=HCDftic1AdA

U2 - 10.1007/978-3-319-55849-3_56

DO - 10.1007/978-3-319-55849-3_56

M3 - Article in proceedings

SN - 978-3-319-55848-6

T3 - Lecture Notes in Computer Science

BT - Applications of Evolutionary Computation

PB - Springer

T2 - Evostar 2017

Y2 - 19 April 2017 through 21 April 2017

ER -

ID: 81921388