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Embodied evolution versus cooperative coevolution in multi-robot optimization: a practical comparison

Authors:

P. Trueba,

A. Prieto,

F. BellasAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 79 - 80

https://doi.org/10.1145/3067695.3076083

Published: 15 July 2017 Publication History

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Abstract

In this paper we show the potentiality of Embodied Evolution in the optimization of general multi-robot systems, as compared to state-of-the-art approaches based on Cooperative Coevolution. The comparison is carried out in a real application problem of coordinating a team of autonomous UAVs for surveillance which, in parallel, must optimize their location accuracy. The results show the advantages of using Embodied Evolution algorithms to notably reduce the number of evolution steps while maintaining the performance level.

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References

[1]

R. Watson, S. Ficici, J. Pollack. 2002. Embodied evolution: Distributing an evolutionary algorithm in a population of robots, Robotics and Autonomous Systems, 39(1), Elsevier, 1--18

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[2]

Z. Yang, K. Tang and Xin Yao. 2008. Multilevel cooperative coevolution for large scale optimization. IEEE Conf on Evolutionary Computation, 1663--1670

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[3]

E. Haasdijk, A.E. Eiben, G. Karafotias. 2010. On-line evolution of robot controllers by an encapsulated evolution strategy. Proc. IEEE CEC2010, IEEE Press, 1--7

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[4]

A. Prieto, F. Bellas, P. Trueba and R.J. Duro. 2016. Real-time optimization of dynamic problems through distributed Embodied Evolution, Integrated Computer-Aided Engineering, vol 23, 237 -- 253

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Cited By

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Cook JTumer KLi XHandl J(2024)Learning Aligned Local Evaluations For Better Credit Assignment In Cooperative CoevolutionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654157(286-294)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654157
Cook JTumer KScheiner TSilva SPaquete L(2023)Leveraging Fitness Critics To Learn Robust TeamworkProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590497(429-437)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590497

Index Terms

Embodied evolution versus cooperative coevolution in multi-robot optimization: a practical comparison
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination
  2. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Evolutionary robotics

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Published In

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 15 July 2017

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EU
Xunta de Galicia and European Regional Development Funds

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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cook JTumer KLi XHandl J(2024)Learning Aligned Local Evaluations For Better Credit Assignment In Cooperative CoevolutionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654157(286-294)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654157
Cook JTumer KScheiner TSilva SPaquete L(2023)Leveraging Fitness Critics To Learn Robust TeamworkProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590497(429-437)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590497

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Challenges in cooperative coevolution of physically heterogeneous robot teams

Echo state networks for embodied evolution in robotic swarms

Improving sampling-based image matting with cooperative coevolution differential evolution algorithm

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