research-article

Right on the MONEE: combining task- and environment-driven evolution

Authors:

Evert Haasdijk,

A. E. EibenAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 207 - 214

https://doi.org/10.1145/2463372.2463396

Published: 06 July 2013 Publication History

Abstract

Evolution can be employed for two goals. Firstly, to provide a force for adaptation to the environment as it does in nature and in many artificial life implementations - this allows the evolving population to survive. Secondly, evolution can provide a force for optimisation as is mostly seen in evolutionary robotics research - this causes the robots to do something useful. We propose the MONEE algorithmic framework as an approach to combine these two facets of evolution: to combine environment-driven and task-driven evolution. To achieve this, MONEE employs environment-driven and task-based parent selection schemes in parallel.

We test this approach in a simulated experimental setting where the robots are tasked to collect two different kinds of puck. MONEE allows the robots to adapt their behaviour to successfully tackle these tasks while ensuring an equitable task distribution at no cost in task performance through a market-based mechanism. In environments that discourage robots performing multiple tasks and in environments where one task is easier than the other, MONEE's market mechanism prevents the population completely focussing on one task.

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Cited By

Neupane AGoodrich M(2024)Resilient swarm behaviors via online evolution and behavior fusionSwarm Intelligence10.1007/s11721-024-00243-wOnline publication date: 17-Aug-2024
https://doi.org/10.1007/s11721-024-00243-w
Steyven AHart EPaechter BBosman P(2017)An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm roboticsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071232(155-162)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071232
Borkowski RHamann H(2017)Evolving robot swarm behaviors by minimizing surpriseProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082548(1679-1680)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082548
Show More Cited By

Index Terms

Right on the MONEE: combining task- and environment-driven evolution
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Publication History

Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

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

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Cited By

Neupane AGoodrich M(2024)Resilient swarm behaviors via online evolution and behavior fusionSwarm Intelligence10.1007/s11721-024-00243-wOnline publication date: 17-Aug-2024
https://doi.org/10.1007/s11721-024-00243-w
Steyven AHart EPaechter BBosman P(2017)An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm roboticsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071232(155-162)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071232
Borkowski RHamann H(2017)Evolving robot swarm behaviors by minimizing surpriseProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082548(1679-1680)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082548
Ripon KJakobsen ETannum CMontanier J(2016)Assessing the effect of self-assembly ports in evolutionary swarm robotics2016 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2016.7850181(1-8)Online publication date: Dec-2016
https://doi.org/10.1109/SSCI.2016.7850181
Steyven AHart EPaechter B(2016)Understanding Environmental Influence in an Open-Ended Evolutionary AlgorithmParallel Problem Solving from Nature – PPSN XIV10.1007/978-3-319-45823-6_86(921-931)Online publication date: 31-Aug-2016
https://doi.org/10.1007/978-3-319-45823-6_86
Hart ESteyven APaechter BEsparcia-Alcázar A(2015)Improving Survivability in Environment-driven Distributed Evolutionary Algorithms through Explicit Relative Fitness and Fitness Proportionate CommunicationProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754688(169-176)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754688
Doncieux SMouret J(2014)Beyond black-box optimization: a review of selective pressures for evolutionary roboticsEvolutionary Intelligence10.1007/s12065-014-0110-x7:2(71-93)Online publication date: 3-Jul-2014
https://doi.org/10.1007/s12065-014-0110-x
Eiben A(2014)In Vivo Veritas: Towards the Evolution of ThingsParallel Problem Solving from Nature – PPSN XIII10.1007/978-3-319-10762-2_3(24-39)Online publication date: 2014
https://doi.org/10.1007/978-3-319-10762-2_3

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