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 outputConference publication


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 languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the 2019 Computing Conference
EditorsKohei Arai, Rahul Bhatia, Supriya Kapoor
Number of pages12
ISBN (Print)9783030228705
Publication statusPublished - 23 Jun 2019
EventComputing Conference, 2019 - London, United Kingdom
Duration: 16 Jul 201917 Jul 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceComputing Conference, 2019
Country/TerritoryUnited Kingdom


  • Computational intelligence
  • Evolutionary computation
  • Hyperheuristics
  • Neural networks
  • Neuroevolution


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