# DENSER: Deep Evolutionary Network Structured Representation

arXiv: https://arxiv.org/abs/1801.01563 · HTML: https://mlsanity.com/paper/1801.01563/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2018.

## Built on by (most cited first, 16 of 16)

- [Interpretable part-whole hierarchies and conceptual-semantic relationships in neural networks](https://mlsanity.com/paper/2203.03282/) (2022)
- [Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars](https://mlsanity.com/paper/2211.01842/) (2022)
- [An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters](https://mlsanity.com/paper/2303.12797/) (2023)
- [CONetV2: Efficient Auto-Channel Size Optimization for CNNs](https://mlsanity.com/paper/2110.06830/) (2021)
- [Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness](https://mlsanity.com/paper/2302.10253/) (2023)
- [Towards Physical Plausibility in Neuroevolution Systems](https://mlsanity.com/paper/2401.17733/) (2024)
- [Evolutionary Retrofitting](https://mlsanity.com/paper/2410.11330/) (2024)
- [PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search](https://mlsanity.com/paper/2101.04185/) (2021)
- [Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE](https://mlsanity.com/paper/2406.14073/) (2024)
- [Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects](https://mlsanity.com/paper/2407.08745/) (2024)
- [Adversarial Robustness Assessment of NeuroEvolution Approaches](https://mlsanity.com/paper/2207.05451/) (2022)
- [SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networks](https://mlsanity.com/paper/2305.10987/) (2023)
- [Towards evolution of Deep Neural Networks through contrastive Self-Supervised learning](https://mlsanity.com/paper/2406.14525/) (2024)
- [MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals](https://mlsanity.com/paper/2410.04992/) (2024)
- [Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms](https://mlsanity.com/paper/2603.17641/) (2026)
- [NERO-Net: A Neuroevolutionary Approach for the Design of Adversarially Robust CNNs](https://mlsanity.com/paper/2603.25517/) (2026)

## Abstract (arXiv)

Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation. The algorithm not only searches for the best network topology (e.g., number of layers, type of layers), but also tunes hyper-parameters, such as, learning parameters or data augmentation parameters. The automatic design is achieved using a representation with two distinct levels, where the outer level encodes the general structure of the network, i.e., the sequence of layers, and the inner level encodes the parameters associated with each layer. The allowed layers and range of the hyper-parameters values are defined by means of a human-readable Context-Free Grammar. DENSER was used to evolve ANNs for CIFAR-10, obtaining an average test accuracy of 94.13%. The networks evolved for the CIFA--10 are tested on the MNIST, Fashion-MNIST, and CIFAR-100; the results are highly competitive, and on the CIFAR-100 we report a test accuracy of 78.75%. To the best of our knowledge, our CIFAR-100 results are the highest performing models generated by methods that aim at the automatic design of Convolutional Neural Networks (CNNs), and are amongst the best for manually designed and fine-tuned CNNs.