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Distributed embodied evolution for collective tasks: parametric analysis of a canonical algorithm

Published: 06 July 2013 Publication History

Abstract

This paper deals with the formal parametric analysis of an evolutionary paradigm inspired in natural evolution, the distributed Embodied Evolution (EE). The main drawback of this approach is in the high parametric sensitivity it presents, being in addition highly task-dependent. With the aim of understanding the parameter relevance in different fitness landscapes, in this work we present a canonical version of a distributed EE algorithm, propose a set of intrinsic parameters that define its response and analyze them in eight collective problem landscapes.

References

[1]
Bredeche, N., Montanier, J. M., Liu, W., Winfield, A. 2012. Environment-driven Distributed Evolutionary Adaptation in a Population of Autonomous Robotic Agents, Mathematical and Computer Modelling of Dynamical Systems, 18: 101--129.
[2]
Karafotias, G., Haasdijk, E., Eiben, A. E. 2011. An algorithm for distributed on-line, on-board evolutionary robotics, In Proceedings GECCO '11, pp. 171--178.
[3]
Prieto, A., Becerra, J. A., Bellas, F., Duro, R. J. 2010. Open-ended Evolution as a means to Self-Organize Heterogeneous Multi-Robot Systems in Real Time, Robotics and Autonomous Systems, vol. 58, pp. 1282--1291.
[4]
Watson, R., Ficici, S., Pollack, J. 2002. Embodied evolution: Distributing an evolutionary algorithm in a population of robots, Robotics and Autonomous Systems, 39(1): 1--18.

Cited By

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  • (2015)Embodied Evolution for Collective Indoor Surveillance and LocationProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2768490(1241-1242)Online publication date: 11-Jul-2015
  • (2015)Applying the canonical distributed Embodied Evolution algorithm in a collective indoor navigation task2015 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2015.7280807(1-8)Online publication date: Jul-2015
  • (2015)Embodied Evolution for Collective Indoor Surveillance and LocationBioinspired Computation in Artificial Systems10.1007/978-3-319-18833-1_15(138-147)Online publication date: 2015

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  1. Distributed embodied evolution for collective tasks: parametric analysis of a canonical algorithm

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    Published In

    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    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

    Publication History

    Published: 06 July 2013

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    Author Tags

    1. algorithm parameterization
    2. collective intelligence
    3. embodied evolution
    4. multiagent systems
    5. open-ended evolution

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    GECCO '13
    Sponsor:
    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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

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    Cited By

    View all
    • (2015)Embodied Evolution for Collective Indoor Surveillance and LocationProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2768490(1241-1242)Online publication date: 11-Jul-2015
    • (2015)Applying the canonical distributed Embodied Evolution algorithm in a collective indoor navigation task2015 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2015.7280807(1-8)Online publication date: Jul-2015
    • (2015)Embodied Evolution for Collective Indoor Surveillance and LocationBioinspired Computation in Artificial Systems10.1007/978-3-319-18833-1_15(138-147)Online publication date: 2015

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