Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology

Jordan J. Bird; Anikó Ekárt; Christopher D. Buckingham; Diego R. Faria

doi:10.1007/978-3-030-22871-2_52

Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology

Jordan J. Bird^*
, Anikó Ekárt
, Christopher D. Buckingham
, Diego R. Faria

^*Corresponding author for this work

Research output: Chapter in Book/Published conference output › Conference publication

17 Citations (Scopus)

Abstract

This paper proposes an approach to selecting the amount of layers and neurons contained within Multilayer Perceptron hidden layers through a single-objective evolutionary approach with the goal of model accuracy. At each generation, a population of Neural Network architectures are created and ranked by their accuracy. The generated solutions are combined in a breeding process to create a larger population, and at each generation the weakest solutions are removed to retain the population size inspired by a Darwinian ‘survival of the fittest’. Multiple datasets are tested, and results show that architectures can be successfully improved and derived through a hyper-heuristic evolutionary approach, in less than 10% of the exhaustive search time. The evolutionary approach was further optimised through population density increase as well as gradual solution max complexity increase throughout the simulation.

Original language	English
Title of host publication	Intelligent Computing - Proceedings of the 2019 Computing Conference
Editors	Kohei Arai, Rahul Bhatia, Supriya Kapoor
Publisher	Springer
Pages	751-762
Number of pages	12
ISBN (Print)	9783030228705
DOIs	https://doi.org/10.1007/978-3-030-22871-2_52
Publication status	Published - 23 Jun 2019
Event	Computing Conference, 2019 - London, United Kingdom Duration: 16 Jul 2019 → 17 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	997
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Computing Conference, 2019
Country/Territory	United Kingdom
City	London
Period	16/07/19 → 17/07/19

Funding

Acknowledgments. This work was supported by the European Commission through the H2020 project EXCELL (https://www.excell-project.eu/), grant No. 691829. This work was also partially supported by the EIT Health GRaCEAGE grant number 18429 awarded to C.D. Buckingham.

Keywords

Computational intelligence
Evolutionary computation
Hyperheuristics
Neural networks
Neuroevolution

Access to Document

10.1007/978-3-030-22871-2_52

Cite this

Bird, J. J., Ekárt, A., Buckingham, C. D., & Faria, D. R. (2019). Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology. In K. Arai, R. Bhatia, & S. Kapoor (Eds.), Intelligent Computing - Proceedings of the 2019 Computing Conference (pp. 751-762). (Advances in Intelligent Systems and Computing; Vol. 997). Springer. https://doi.org/10.1007/978-3-030-22871-2_52

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title = "Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology",

abstract = "This paper proposes an approach to selecting the amount of layers and neurons contained within Multilayer Perceptron hidden layers through a single-objective evolutionary approach with the goal of model accuracy. At each generation, a population of Neural Network architectures are created and ranked by their accuracy. The generated solutions are combined in a breeding process to create a larger population, and at each generation the weakest solutions are removed to retain the population size inspired by a Darwinian {\textquoteleft}survival of the fittest{\textquoteright}. Multiple datasets are tested, and results show that architectures can be successfully improved and derived through a hyper-heuristic evolutionary approach, in less than 10\% of the exhaustive search time. The evolutionary approach was further optimised through population density increase as well as gradual solution max complexity increase throughout the simulation.",

keywords = "Computational intelligence, Evolutionary computation, Hyperheuristics, Neural networks, Neuroevolution",

author = "Bird, \{Jordan J.\} and Anik{\'o} Ek{\'a}rt and Buckingham, \{Christopher D.\} and Faria, \{Diego R.\}",

year = "2019",

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doi = "10.1007/978-3-030-22871-2\_52",

language = "English",

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series = "Advances in Intelligent Systems and Computing",

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Bird, JJ, Ekárt, A , Buckingham, CD & Faria, DR 2019, Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology. in K Arai, R Bhatia & S Kapoor (eds), Intelligent Computing - Proceedings of the 2019 Computing Conference. Advances in Intelligent Systems and Computing, vol. 997, Springer, pp. 751-762, Computing Conference, 2019, London, United Kingdom, 16/07/19. https://doi.org/10.1007/978-3-030-22871-2_52

Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology. / Bird, Jordan J.; Ekárt, Anikó ; Buckingham, Christopher D. et al.
Intelligent Computing - Proceedings of the 2019 Computing Conference. ed. / Kohei Arai; Rahul Bhatia; Supriya Kapoor. Springer, 2019. p. 751-762 (Advances in Intelligent Systems and Computing; Vol. 997).

Research output: Chapter in Book/Published conference output › Conference publication

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Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology

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