# NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm

arXiv: https://arxiv.org/abs/1810.03522 · HTML: https://mlsanity.com/paper/1810.03522/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers](https://mlsanity.com/paper/2106.06560/) (2021)
- [Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization](https://mlsanity.com/paper/2105.01015/) (2021)
- [Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search](https://mlsanity.com/paper/2204.05941/) (2022)
- [Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2109.07582/) (2021)
- [GP-NAS-ensemble: a model for NAS Performance Prediction](https://mlsanity.com/paper/2301.09231/) (2023)
- [SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models](https://mlsanity.com/paper/2502.20422/) (2025)
- [Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search](https://mlsanity.com/paper/2103.00363/) (2021)
- [Coarse-to-Fine Searching for Efficient Generative Adversarial Networks](https://mlsanity.com/paper/2104.09223/) (2021)
- [Tackling Neural Architecture Search With Quality Diversity Optimization](https://mlsanity.com/paper/2208.00204/) (2022)
- [An Analysis of Super-Net Heuristics in Weight-Sharing NAS](https://mlsanity.com/paper/2110.01154/) (2021)
- [TS-ENAS:Two-Stage Evolution for Cell-based Network Architecture Search](https://mlsanity.com/paper/2310.09525/) (2023)
- [Reinforcement Learning Approach for Mapping Applications to Dataflow-Based Coarse-Grained Reconfigurable Array](https://mlsanity.com/paper/2205.13675/) (2022)
- [Asynchronous Evolution of Deep Neural Network Architectures](https://mlsanity.com/paper/2308.04102/) (2023)

## Abstract (arXiv)

This paper introduces NSGA-Net -- an evolutionary approach for neural architecture search (NAS). NSGA-Net is designed with three goals in mind: (1) a procedure considering multiple and conflicting objectives, (2) an efficient procedure balancing exploration and exploitation of the space of potential neural network architectures, and (3) a procedure finding a diverse set of trade-off network architectures achieved in a single run. NSGA-Net is a population-based search algorithm that explores a space of potential neural network architectures in three steps, namely, a population initialization step that is based on prior-knowledge from hand-crafted architectures, an exploration step comprising crossover and mutation of architectures, and finally an exploitation step that utilizes the hidden useful knowledge stored in the entire history of evaluated neural architectures in the form of a Bayesian Network. Experimental results suggest that combining the dual objectives of minimizing an error metric and computational complexity, as measured by FLOPs, allows NSGA-Net to find competitive neural architectures. Moreover, NSGA-Net achieves error rate on the CIFAR-10 dataset on par with other state-of-the-art NAS methods while using orders of magnitude less computational resources. These results are encouraging and shows the promise to further use of EC methods in various deep-learning paradigms.