Convolutional neural network surrogate-assisted GOMEA

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6 Citations (Scopus)


We introduce a novel surrogate-assisted Genetic Algorithm (GA) for expensive optimization of problems with discrete categorical variables. Specifically, we leverage the strengths of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA), a state-of-the-art GA, and, for the first time, propose to use a convolutional neural network (CNN) as a surrogate model. We propose to train the model on pairwise fitness differences to decrease the number of evaluated solutions that is required to achieve adequate surrogate model training. In providing a proof of principle, we consider relatively standard CNNs, and demonstrate that their capacity is already sufficient to accurately learn fitness landscapes of various well-known benchmark functions. The proposed CS-GOMEA is compared with GOMEA and the widely-used Bayesian-optimization-based expensive optimization frameworks SMAC and Hyperopt, in terms of the number of evaluations that is required to achieve the optimum. In our experiments on binary problems with dimensionalities up to 400 variables, CS-GOMEA always found the optimum, whereas SMAC and Hyperopt failed for problem sizes over 16 variables. Moreover, the number of evaluated solutions required by CS-GOMEA to find the optimum was found to scale much better than GOMEA.

Original languageEnglish
Title of host publicationGECCO 2019
Subtitle of host publicationProceedings of the 2019 Genetic and Evolutionary Computation Conference
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Print)978-1-4503-6111-8
Publication statusPublished - 2019
EventGECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019


ConferenceGECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference
Abbreviated titleGECCO '19
CountryCzech Republic


  • Convolutional neural network
  • Discrete optimization
  • Expensive optimization
  • Surrogate model
  • Surrogate-assisted GA


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