# Meta knowledge assisted Evolutionary Neural Architecture Search

arXiv: https://arxiv.org/abs/2504.21545 · HTML: https://mlsanity.com/paper/2504.21545/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 74 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [Neural Architecture Optimization](https://mlsanity.com/paper/1808.07233/) (2018)
- [A Survey on Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2008.10937/) (2020)
- [Genetic CNN](https://mlsanity.com/paper/1703.01513/) (2017)
- [Evolved Policy Gradients](https://mlsanity.com/paper/1802.04821/) (2018)
- [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)
- [Neural Architecture Search for Spiking Neural Networks](https://mlsanity.com/paper/2201.10355/) (2022)
- [Automatically designing CNN architectures using genetic algorithm for image classification](https://mlsanity.com/paper/1808.03818/) (2018)
- [CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning](https://mlsanity.com/paper/2202.05613/) (2022)
- [You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization](https://mlsanity.com/paper/1811.01567/) (2018)
- [UNAS: Differentiable Architecture Search Meets Reinforcement Learning](https://mlsanity.com/paper/1912.07651/) (2019)
- [Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment](https://mlsanity.com/paper/2208.04321/) (2022)
- [Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity Evaluation](https://mlsanity.com/paper/2108.04541/) (2021)
- [Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2109.07582/) (2021)
- [Partial Connection Based on Channel Attention for Differentiable Neural Architecture Search](https://mlsanity.com/paper/2208.00791/) (2022)
- [IGD Indicator-based Evolutionary Algorithm for Many-objective Optimization Problems](https://mlsanity.com/paper/1802.08792/) (2018)

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

- [Rank-Reliable Teacher-Guided Fitness Approximation for Expensive Evolutionary Optimization: A TinyML Architecture Search Study](https://mlsanity.com/paper/2609.30553/) (2026)

## Abstract (arXiv)

Evolutionary computation (EC)-based neural architecture search (NAS) has achieved remarkable performance in the automatic design of neural architectures. However, the high computational cost associated with evaluating searched architectures poses a challenge for these methods, and a fixed form of learning rate (LR) schedule means greater information loss on diverse searched architectures. This paper introduces an efficient EC-based NAS method to solve these problems via an innovative meta-learning framework. Specifically, a meta-learning-rate (Meta-LR) scheme is used through pretraining to obtain a suitable LR schedule, which guides the training process with lower information loss when evaluating each individual. An adaptive surrogate model is designed through an adaptive threshold to select the potential architectures in a few epochs and then evaluate the potential architectures with complete epochs. Additionally, a periodic mutation operator is proposed to increase the diversity of the population, which enhances the generalizability and robustness. Experiments on CIFAR-10, CIFAR-100, and ImageNet1K datasets demonstrate that the proposed method achieves high performance comparable to that of many state-of-the-art peer methods, with lower computational cost and greater robustness.