# Evolutionary Architecture Search For Deep Multitask Networks

arXiv: https://arxiv.org/abs/1803.03745 · HTML: https://mlsanity.com/paper/1803.03745/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2018.

## Built on by (most cited first, 23 of 23)

- [Universal Representations: A Unified Look at Multiple Task and Domain Learning](https://mlsanity.com/paper/2204.02744/) (2022)
- [AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning](https://mlsanity.com/paper/2110.13076/) (2021)
- [A Survey on Computationally Efficient Neural Architecture Search](https://mlsanity.com/paper/2206.01520/) (2022)
- [Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks](https://mlsanity.com/paper/2101.06507/) (2021)
- [Learning Versatile Neural Architectures by Propagating Network Codes](https://mlsanity.com/paper/2103.13253/) (2021)
- [Dynamic Neural Network for Multi-Task Learning Searching across Diverse Network Topologies](https://mlsanity.com/paper/2303.06856/) (2023)
- [Multi-task Learning with 3D-Aware Regularization](https://mlsanity.com/paper/2310.00986/) (2023)
- [Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey](https://mlsanity.com/paper/2411.05614/) (2024)
- [Multi-Task Learning for Visual Scene Understanding](https://mlsanity.com/paper/2203.14896/) (2022)
- [When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review](https://mlsanity.com/paper/2307.14382/) (2023)
- [Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search](https://mlsanity.com/paper/2210.01384/) (2022)
- [Software/Hardware Co-design for Multi-modal Multi-task Learning in Autonomous Systems](https://mlsanity.com/paper/2104.04000/) (2021)
- [A Novel Evolutionary Algorithm for Hierarchical Neural Architecture Search](https://mlsanity.com/paper/2107.08484/) (2021)
- [Resolving Token-Space Gradient Conflicts: Token Space Manipulation for Transformer-Based Multi-Task Learning](https://mlsanity.com/paper/2507.07485/) (2025)
- [Revisit the Imbalance Optimization in Multi-task Learning: An Experimental Analysis](https://mlsanity.com/paper/2509.23915/) (2025)
- [Visual Attention Prompted Prediction and Learning](https://mlsanity.com/paper/2310.08420/) (2023)
- [3D-Aware Multi-Task Learning with Cross-View Correlations for Dense Scene Understanding](https://mlsanity.com/paper/2511.20646/) (2025)
- [Deep Safe Multi-Task Learning](https://mlsanity.com/paper/2111.10601/) (2021)
- [Bayesian Quadrature for Neural Ensemble Search](https://mlsanity.com/paper/2303.08874/) (2023)
- [Reliable and Fast Recurrent Neural Network Architecture Optimization](https://mlsanity.com/paper/2106.15295/) (2021)
- [Asynchronous Evolution of Deep Neural Network Architectures](https://mlsanity.com/paper/2308.04102/) (2023)
- [GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation](https://mlsanity.com/paper/2310.10211/) (2023)
- [Vision Generalist Model: A Survey](https://mlsanity.com/paper/2506.09954/) (2025)

## Abstract (arXiv)

Multitask learning, i.e. learning several tasks at once with the same neural network, can improve performance in each of the tasks. Designing deep neural network architectures for multitask learning is a challenge: There are many ways to tie the tasks together, and the design choices matter. The size and complexity of this problem exceeds human design ability, making it a compelling domain for evolutionary optimization. Using the existing state of the art soft ordering architecture as the starting point, methods for evolving the modules of this architecture and for evolving the overall topology or routing between modules are evaluated in this paper. A synergetic approach of evolving custom routings with evolved, shared modules for each task is found to be very powerful, significantly improving the state of the art in the Omniglot multitask, multialphabet character recognition domain. This result demonstrates how evolution can be instrumental in advancing deep neural network and complex system design in general.