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A Cell-Based Fast Memetic Algorithm for Automated Convolutional Neural Architecture Design.

Junwei Dong, Boyu Hou, Liang Feng

IEEE Transactions on Neural Networks and Learning Systems

|March 17, 2022

Summary

This summary is machine-generated.

This study introduces an efficient memetic algorithm (MA) for automated convolutional neural network (CNN) architecture search. The novel approach significantly reduces computational cost, making NAS more practical for designing efficient neural networks.

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Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision

Background:

Neural Architecture Search (NAS) automates neural network design, traditionally a manual process.
Evolutionary Optimization (EO) is effective for NAS but often computationally expensive.
Existing EO methods for NAS lack practical efficiency.

Purpose of the Study:

To propose an efficient memetic algorithm (MA) for automated convolutional neural network (CNN) architecture search.
To address the computational expense of existing evolutionary optimization (EO) algorithms for NAS.
To enhance the practicality and efficiency of NAS for CNN design.

Main Methods:

Developed a novel cell-based architecture search space for CNNs.
Introduced new global and local search operators tailored for CNN architecture design.
Implemented a one-epoch-based performance estimation strategy without pre-trained models for efficiency.

Main Results:

The proposed MA demonstrated efficacy in automated CNN architecture design.
Comprehensive empirical studies validated the approach against 34 state-of-the-art algorithms.
The method was tested on widely used CIFAR10 and CIFAR100 datasets.

Conclusions:

The efficient memetic algorithm offers a practical solution for automated CNN architecture search.
The novel search space and operators contribute to improved NAS efficiency.
The one-epoch evaluation strategy significantly reduces computational overhead.