Optimization of multi-robot sumo fight simulation by a genetic algorithm to identify dominant robot capabilities
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Publication date
2019
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Collections
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04B - Conference paper
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2019 IEEE Congress on Evolutionary Computation (CEC 2019). Proceedings
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Issue / Number
Pages / Duration
490-496
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Place of publication / Event location
Wellington
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Abstract
This paper analyzes the multirobot sumo fight simulation. This simulation is based on a computational model of several sumo fighters, which physically interact while trying to move the opponent out of the arena (lost fight). The problem is optimized using a genetic algorithm (GA), where the capabilities of not only one particular robot but of all robots simultaneously are improved. In this particular problem setup, the problem definition changes depending on the optimization path, because all robots also get better, competing against each other. The influence of different operators of the GA is investigated and compared. This paper raises the questions, which genetically controlled capabilities (e.g. size, speed) are dominant over time and how they can be identified by a sensitivity analysis using a GA. The results shed light on which parameters are dominant. This experiment typically opens up interesting fields of further research, especially about how to address optimization problems, where the optimization process influences the search space and how to eliminate the factor of randomness.
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Event
2019 IEEE Congress on Evolutionary Computation (CEC 2019)
Exhibition start date
Exhibition end date
Conference start date
10.06.2019
Conference end date
13.06.2019
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ISBN
978-1-7281-2153-6
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Language
English
Created during FHNW affiliation
Yes
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Publication status
Published
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Peer review of the complete publication
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Closed
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Citation
Lehner, J. E., Dornberger, R., Simic, R., & Hanne, T. (2019). Optimization of multi-robot sumo fight simulation by a genetic algorithm to identify dominant robot capabilities. 2019 IEEE Congress on Evolutionary Computation (CEC 2019). Proceedings, 490–496. https://doi.org/10.1109/CEC.2019.8790367