Parallel Distributed Implementation of Neuroevolution of Augmenting Topologies in Continuous Control Tasks

This paper proposes a novel distributed implementation of neuroevolution of augmenting topologies method, which, considering the availability of sufficient computational resources, allows drastically speed up the process of optimal neural network configuration search. The proposed solution includes...

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Bibliographic Details
Published in:	2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT) pp. 267 - 271
Main Authors:	Achour, Illia, Doroshenko, Anatoliy
Format:	Conference Proceeding
Language:	English
Published:	IEEE 15.12.2021
Subjects:	artificial neural networks Benchmark testing cloud computing Computational efficiency genetic algorithms Genomics NEAT Network topology Neural networks neuroevolution of augmenting topologies parallel distributed computing Process control reinforcement learning Topology
Online Access:	Get full text
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Summary:	This paper proposes a novel distributed implementation of neuroevolution of augmenting topologies method, which, considering the availability of sufficient computational resources, allows drastically speed up the process of optimal neural network configuration search. The proposed solution includes batch genome evaluation for the purpose of performance optimization, fair, and even computational resources usage. The benchmarking shows that the generated neural networks evaluation process can give orders of magnitude increase of efficiency on the demonstrated continuous control task and computational environment.
DOI:	10.1109/ATIT54053.2021.9678858