# EENA: Efficient Evolution of Neural Architecture

arXiv: https://arxiv.org/abs/1905.07320 · HTML: https://mlsanity.com/paper/1905.07320/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

## Built on by (most cited first, 10 of 10)

- [Hierarchical Self-supervised Augmented Knowledge Distillation](https://mlsanity.com/paper/2107.13715/) (2021)
- [AutoBERT-Zero: Evolving BERT Backbone from Scratch](https://mlsanity.com/paper/2107.07445/) (2021)
- [SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NAS](https://mlsanity.com/paper/2403.04161/) (2024)
- [PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2204.12726/) (2022)
- [Efficiently Distilling LLMs for Edge Applications](https://mlsanity.com/paper/2404.01353/) (2024)
- [Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search](https://mlsanity.com/paper/2103.00363/) (2021)
- [B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search](https://mlsanity.com/paper/2202.03005/) (2022)
- [Efficient Neural Architecture Search with Performance Prediction](https://mlsanity.com/paper/2108.01854/) (2021)
- [NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks](https://mlsanity.com/paper/2206.00402/) (2022)
- [Zero-Shot Neural Network Evaluation with Sample-Wise Activation Patterns](https://mlsanity.com/paper/2605.07378/) (2026)

## Abstract (arXiv)

Latest algorithms for automatic neural architecture search perform remarkable but are basically directionless in search space and computational expensive in training of every intermediate architecture. In this paper, we propose a method for efficient architecture search called EENA (Efficient Evolution of Neural Architecture). Due to the elaborately designed mutation and crossover operations, the evolution process can be guided by the information have already been learned. Therefore, less computational effort will be required while the searching and training time can be reduced significantly. On CIFAR-10 classification, EENA using minimal computational resources (0.65 GPU-days) can design highly effective neural architecture which achieves 2.56% test error with 8.47M parameters. Furthermore, the best architecture discovered is also transferable for CIFAR-100.