A Survey of Evolutionary Optimization-based Neural Ar-chitecture Search
This paper explores the evolutionary computation(EC)-based neural architecture search(NAS)algorithms,which can automatically discover neural network structures suitable for specific tasks by simulating the process of biological evolution.This paper reviews the advantages of EC-based NAS algorithms in global search,adaptability and flexibility for both continuous and discrete optimization problems.The application of EC in NAS,especially in consideration of its potential in searching in discrete and high-dimensional space,is introduced in detail.Furthermore,this paper summarizes the main advances and challenges in EC-based NAS algorithms.Finally,this paper looks forward to the prospect of EC-based NAS algorithms in driving more breakthroughs in the field of neural network architecture search.Future research directions include improvement of the computa-tional efficiency of the algorithms,introduction of intelligent search strategies,solution of the problem of high-dimensional search space,and the integration with deep learning.
万方数据
维普
