# A Continuous Encoding-Based Representation for Efficient Multi-Fidelity Multi-Objective Neural Architecture Search

arXiv: https://arxiv.org/abs/2509.01943 · HTML: https://mlsanity.com/paper/2509.01943/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 78 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017)
- [PDEBENCH: An Extensive Benchmark for Scientific Machine Learning](https://mlsanity.com/paper/2210.07182/) (2022)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [A Survey on Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2008.10937/) (2020)
- [Genetic CNN](https://mlsanity.com/paper/1703.01513/) (2017)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [CARS: Continuous Evolution for Efficient Neural Architecture Search](https://mlsanity.com/paper/1909.04977/) (2019)
- [NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search](https://mlsanity.com/paper/2007.10396/) (2020)
- [IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks](https://mlsanity.com/paper/1912.05763/) (2019)
- [Evolving Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1710.10741/) (2017)
- [SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes](https://mlsanity.com/paper/2305.13998/) (2023)
- [Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search](https://mlsanity.com/paper/1901.01074/) (2019)
- [Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment](https://mlsanity.com/paper/2208.04321/) (2022)
- [Multi-fidelity Neural Architecture Search with Knowledge Distillation](https://mlsanity.com/paper/2006.08341/) (2020)
- [Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity Evaluation](https://mlsanity.com/paper/2108.04541/) (2021)
- [Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm](https://mlsanity.com/paper/2010.15560/) (2020)
- [BTS-DSN: Deeply Supervised Neural Network with Short Connections for Retinal Vessel Segmentation](https://mlsanity.com/paper/1803.03963/) (2018)
- [Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2109.07582/) (2021)
- [Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation](https://mlsanity.com/paper/2208.06820/) (2022)
- [FastFlow: AI for Fast Urban Wind Velocity Prediction](https://mlsanity.com/paper/2211.12035/) (2022)

## Built on by (most cited first, 1 of 1)

- [Out-of-Distribution Generalization for Neural Physics Solvers](https://mlsanity.com/paper/2601.19091/) (2026)

## Abstract (arXiv)

Neural architecture search (NAS) is an attractive approach to automate the design of optimized architectures but is constrained by high computational budget, especially when optimizing for multiple, important conflicting objectives. To address this, an adaptive Co-Kriging-assisted multi-fidelity multi-objective NAS algorithm is proposed to further reduce the computational cost of NAS by incorporating a clustering-based local multi-fidelity infill sampling strategy, enabling efficient exploration of the search space for faster convergence. This algorithm is further accelerated by the use of a novel continuous encoding method to represent the connections of nodes in each cell within a generalized cell-based U-Net backbone, thereby decreasing the search dimension (number of variables). Results indicate that the proposed NAS algorithm outperforms previously published state-of-the-art methods under limited computational budget on three numerical benchmarks, a 2D Darcy flow regression problem and a CHASE_DB1 biomedical image segmentation problem. The proposed method is subsequently used to create a wind velocity regression model with application in urban modelling, with the found model able to achieve good prediction with less computational complexity. Further analysis revealed that the NAS algorithm independently identified principles undergirding superior U-Net architectures in other literature, such as the importance of allowing each cell to incorporate information from prior cells.