Distributed embodied evolution over networks

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In several network problems the optimal behavior of the agents (i.e., the nodes of the network) is not known before deployment. Furthermore, the agents might be required to adapt, i.e. change their behavior based on the environment conditions. In these scenarios, offline optimization is usually costly and inefficient, while online methods might be more suitable. In this work, we use a distributed Embodied Evolution approach to optimize spatially distributed, locally interacting agents by allowing them to exchange their behavior parameters and learn from each other to adapt to a certain task within a given environment. Our results on several test scenarios show that the local exchange of information, performed by means of crossover of behavior parameters with neighbors, allows the network to conduct the optimization process more efficiently than the cases where local interactions are not allowed, even when there are large differences on the optimal behavior parameters within each agent's neighborhood. (C) 2020 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER
Issue Date
2021-03
Language
English
Article Type
Article
Citation

APPLIED SOFT COMPUTING, v.101

ISSN
1568-4946
DOI
10.1016/j.asoc.2020.106993
URI
http://hdl.handle.net/10203/281732
Appears in Collection
RIMS Journal Papers
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