Neural Architecture Search has shown potential to automate the design of neural networks. Deep Reinforcement Learning based agents can learn complex architectural patterns, as well as explore a vast and compositional search space. On the other hand, evolutionary algorithms offer higher sample efficiency, which is critical for such a resource intensive application. In order to capture the best of both worlds, we propose a class of Evolutionary-Neural hybrid agents (Evo-NAS). We show that the Evo-NAS agent outperforms both neural and evolutionary agents when applied to architecture search for a suite of text and image classification benchmarks. On a high-complexity architecture search space for image classification, the Evo-NAS agent surpasses the accuracy achieved by commonly used agents with only 1/3 of the search cost.
In this paper, the authors proposed to combine both NN-based NAS and Aging EVO to get the benefit of both world: good global and local sample efficiency. The main idea is to use Aging EVO algorithm to guide the overall search process, and a NN predicting the final performance is used to guide the mutation process. The combined EVO-NAS has showed consistent good performance over a range of tasks.
Overall, while the novelty of the paper is not exceptional, since it is a rather straightforward combination of two existing approaches, the end-results is promising. I would like to see more in-depth analysis on the combined algorithm to validate authors' hypothesis on why EVO-NAS works better. More detailed comments can be found below.
1. The experiment on the synthetic task is not very helpful, since the domain can be far apart from the real NAS applications. One evidence is that the NN significantly outperform EVO in this task but not in the other tasks.
2. In difficult tasks, the proposed EVO-NAS and the original Aging EVO are very close in the first few hundreds of trials, however, later the gap remains the same. It would be interesting to see if we can eliminate the gap by adding the NN component in the middle of the Aging EVO experiment (e.g., at the point of 2000 trials).
3. I am also curious about the difference between the NN learned from Neural agent vs. those learned from EVO-NAS agent. Moreover, do we need a NN for the EVO-NAS? Or something simpler would be sufficient to guide the search.