Hyper-parameter Optimisation by Restrained Stochastic Hill Climbing

Authors: Stubbs, R. and Rostami, S.

http://eprints.bournemouth.ac.uk/32508/

Start date: 4 September 2019

This data was imported from Scopus:

Authors: Stubbs, R., Wilson, K. and Rostami, S.

http://eprints.bournemouth.ac.uk/32508/

Journal: Advances in Intelligent Systems and Computing

Volume: 1043

Pages: 189-200

eISSN: 2194-5365

ISBN: 9783030299323

ISSN: 2194-5357

DOI: 10.1007/978-3-030-29933-0_16

© 2020, Springer Nature Switzerland AG. Machine learning practitioners often refer to hyper-parameter optimisation (HPO) as an art form and a skill that requires intuition and experience; Neuroevolution (NE) typically employs a combination of manual and evolutionary approaches for HPO. This paper explores the integration of a stochastic hill climbing approach for HPO within a NE algorithm. We empirically show that HPO by restrained stochastic hill climbing (HORSHC) is more effective than manual and pure evolutionary HPO. Empirical evidence is derived from a comparison of: (1) a NE algorithm that solely optimises hyper-parameters through evolution and (2) a number of derived algorithms with random search optimisation integration for optimising the hyper-parameters of a Neural Network. Through statistical analysis of the experimental results it has been revealed that random initialisation of hyper-parameters does not significantly affect the final performance of the Neural Networks evolved. However, HORSHC, a novel optimisation approach proposed in this paper has been proven to significantly out-perform the NE control algorithm. HORSHC presents itself as a solution that is computationally comparable in terms of both time and complexity as well as outperforming the control algorithm.

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