# Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2410.00129 · HTML: https://mlsanity.com/paper/2410.00129/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [On Pre-Trained Image Features and Synthetic Images for Deep Learning](https://mlsanity.com/paper/1710.10710/) (2017)
- [Online 3D Bin Packing with Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2006.14978/) (2020)
- [Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations](https://mlsanity.com/paper/1906.11031/) (2019)
- [Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation](https://mlsanity.com/paper/1803.01199/) (2018)
- [Learning Practically Feasible Policies for Online 3D Bin Packing](https://mlsanity.com/paper/2108.13680/) (2021)
- [Lung Segmentation from Chest X-rays using Variational Data Imputation](https://mlsanity.com/paper/2005.10052/) (2020)
- [Deep Convolutional Neural Networks as Generic Feature Extractors](https://mlsanity.com/paper/1710.02286/) (2017)
- [Online 3D Bin Packing Reinforcement Learning Solution with Buffer](https://mlsanity.com/paper/2208.07123/) (2022)
- [MeROS: SysML-based Metamodel for ROS-based Systems](https://mlsanity.com/paper/2303.08254/) (2023)
- [A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem](https://mlsanity.com/paper/1804.06896/) (2018)
- [An intent-based approach for creating assistive robots' control systems](https://mlsanity.com/paper/2005.12106/) (2020)

## Abstract (arXiv)

The present study covers an approach to neural architecture search (NAS) using Cartesian genetic programming (CGP) for the design and optimization of Convolutional Neural Networks (CNNs). In designing artificial neural networks, one crucial aspect of the innovative approach is suggesting a novel neural architecture. Currently used architectures have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In this work, we use pure Genetic Programming Approach to design CNNs, which employs only one genetic operation, i.e., mutation. In the course of preliminary experiments, our methodology yields promising results.