Which hyperparameters to optimise? an investigation of evolutionary hyperparameter optimisation in graph neural network for molecular property prediction

Yingfang Yuan, Wenjun Wang, Wei Pang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Most GNNs for molecular property prediction are proposed based on the idea of learning the representations for the nodes by aggregating the information of their neighbour nodes in graph layers. Then, the representations can be passed to subsequent task-specific layers to deal with individual downstream tasks. Facing real-world molecular problems, the hyperparameter optimisation for those layers are vital. In this research, we focus on the impact of selecting two types of GNN hyperparameters, those belonging to graph layers and those of task-specific layers, on the performance of GNN for molecular property prediction. In our experiments, we employed a state-of-the-art evolutionary algorithm (i.e., CMA-ES) for HPO. The results reveal that optimising the two types of hyperparameters separately can improve GNNs' performance, but optimising both types of hyperparameters simultaneously will lead to predominant improvements.
Original languageEnglish
Title of host publicationGECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery
Pages1403–1404
Number of pages2
ISBN (Print) 9781450383516
DOIs
Publication statusPublished - 7 Jul 2021
Event11th Workshop on Evolutionary Computation for the Automated Design of Algorithms at GECCO 2021 - online
Duration: 10 Jul 202114 Jul 2021
https://bonsai.auburn.edu/ecada/

Workshop

Workshop11th Workshop on Evolutionary Computation for the Automated Design of Algorithms at GECCO 2021
Abbreviated titleECADA 2021
Period10/07/2114/07/21
Internet address

Keywords

  • evolutionary computation
  • graph neural networks
  • hyperparameter optimisation
  • molecular property prediction

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computational Theory and Mathematics

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