Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor

By Yanan Sun, Handing Wang, Bing Xue, Yaochu Jin, Gary G. Yen, Mengjie Zhang. 2020

266 citations recorded by the source corpus.

Convolutional neural networks (CNNs) have shown remarkable performance in various real-world applications. Unfortunately, the promising performance of CNNs can be achieved only when their architectures are optimally constructed. The architectures of state-of-the-art CNNs are typically handcrafted with extensive expertise in both CNNs and the investigated data, which consequently hampers the widespread adoption of CNNs for less experienced users. Evolutionary deep learning (EDL) is able to automatically design the best CNN architectures without much expertise. However, the existing EDL algorithms generally evaluate the fitness of a new architecture by training from scratch, resulting in the prohibitive computational cost even operated on high-performance computers. In this paper, an end-to-end offline performance predictor based on the random forest is proposed to accelerate the fitness evaluation in EDL. The proposed performance predictor shows the promising performance in term of the classification accuracy and the consumed computational resources when compared with 18 state-of-the-art peer competitors by integrating into an existing EDL algorithm as a case study. The proposed performance predictor is also compared with the other two representatives of existing performance predictors. The experimental results show the proposed performance predictor not only significantly speeds up the fitness evaluations but also achieves the best prediction among the peer performance predictors.

Subfields: Artificial Intelligence, Neural Architecture Search & AutoML

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hangyu Zhu
2. Yaochu Jin
3. Handing Wang
4. Xing-yi Zhang
5. Ran Cheng
6. Ye Tian
7. J. Branke
8. Torsten Hildebrandt

In Neural Architecture Search & AutoML

1. Hangyu Zhu
2. Yaochu Jin

Citations in the corpus

• Real-Time Federated Evolutionary Neural Architecture Search (2020, positive): “To reduce the computational cost, surrogate ensembles have been introduced in evolutionary NAS, which has been shown promising in reducing the computational complexity without deteriorating the learning performance [16].”