# A Genetic Programming Approach to Designing Convolutional Neural Network Architectures

arXiv: https://arxiv.org/abs/1704.00764 · HTML: https://mlsanity.com/paper/1704.00764/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2017.

## Built on by (most cited first, 30 of 59)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment](https://mlsanity.com/paper/2208.04321/) (2022)
- [Knowledge Fusion By Evolving Weights of Language Models](https://mlsanity.com/paper/2406.12208/) (2024)
- [One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking](https://mlsanity.com/paper/2104.00597/) (2021)
- [A Survey on Computationally Efficient Neural Architecture Search](https://mlsanity.com/paper/2206.01520/) (2022)
- [iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization](https://mlsanity.com/paper/2108.11014/) (2021)
- [NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters](https://mlsanity.com/paper/2110.10165/) (2021)
- [Less is More: Proxy Datasets in NAS approaches](https://mlsanity.com/paper/2203.06905/) (2022)
- [A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends](https://mlsanity.com/paper/2209.06399/) (2022)
- [Dynamic Neural Network for Multi-Task Learning Searching across Diverse Network Topologies](https://mlsanity.com/paper/2303.06856/) (2023)
- [PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2204.12726/) (2022)
- [A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks](https://mlsanity.com/paper/2101.09300/) (2021)
- [Search For Deep Graph Neural Networks](https://mlsanity.com/paper/2109.10047/) (2021)
- [Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence](https://mlsanity.com/paper/2301.04020/) (2022)
- [Robust NAS under adversarial training: benchmark, theory, and beyond](https://mlsanity.com/paper/2403.13134/) (2024)
- [Enhanced Gradient for Differentiable Architecture Search](https://mlsanity.com/paper/2103.12529/) (2021)
- [Graph-based Neural Architecture Search with Operation Embeddings](https://mlsanity.com/paper/2105.04885/) (2021)
- [Manas: Mining Software Repositories to Assist AutoML](https://mlsanity.com/paper/2112.03395/) (2021)
- [Exploring the Intersection between Neural Architecture Search and Continual Learning](https://mlsanity.com/paper/2206.05625/) (2022)
- [ObfuNAS: A Neural Architecture Search-based DNN Obfuscation Approach](https://mlsanity.com/paper/2208.08569/) (2022)
- [AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images](https://mlsanity.com/paper/2208.13246/) (2022)
- [DQNAS: Neural Architecture Search using Reinforcement Learning](https://mlsanity.com/paper/2301.06687/) (2023)
- [AutoML for Large Capacity Modeling of Meta's Ranking Systems](https://mlsanity.com/paper/2311.07870/) (2023)
- [Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems](https://mlsanity.com/paper/2305.07135/) (2023)
- [A Novel Framework for Neural Architecture Search in the Hill Climbing Domain](https://mlsanity.com/paper/2102.12985/) (2021)
- [HALF: Holistic Auto Machine Learning for FPGAs](https://mlsanity.com/paper/2106.14771/) (2021)
- [Experiments on Properties of Hidden Structures of Sparse Neural Networks](https://mlsanity.com/paper/2107.12917/) (2021)
- [PSO-Convolutional Neural Networks with Heterogeneous Learning Rate](https://mlsanity.com/paper/2205.10456/) (2022)
- [An Adaptive and Stability-Promoting Layerwise Training Approach for Sparse Deep Neural Network Architecture](https://mlsanity.com/paper/2211.06860/) (2022)
- [A Survey on Multi-Objective based Parameter Optimization for Deep Learning](https://mlsanity.com/paper/2305.10014/) (2023)

## Abstract (arXiv)

The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.