Lightweight Multi-Objective and Many-Objective Problem Formulations for Evolutionary Neural Architecture Search with the Training-Free Performance Metric Synaptic Flow

Neural architecture search (NAS) with naive problem formulations and applications of conventional search algorithms often incur prohibitive search costs due to the evaluations of many candidate architectures. For each architecture, its accuracy performance can be properly evaluated after hundreds (o...

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Vydané v:	Informatica (Ljubljana) Ročník 47; číslo 3; s. 303 - 314
Hlavní autori:	Vo, An, Pham, Tan Ngoc, Nguyen, Van Bich, Luong, Ngoc Hoang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Ljubljana Slovenian Society Informatika / Slovensko drustvo Informatika 01.09.2023
Predmet:	Accuracy Artificial neural networks Business metrics Evolutionary algorithms Floating point arithmetic Genetic algorithms Multiple objective analysis Network latency Neural networks Objectives Optimization Performance evaluation Search algorithms Sorting algorithms Training
ISSN:	0350-5596, 1854-3871
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