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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Veröffentlicht in:	Informatica (Ljubljana) Jg. 47; H. 3; S. 303 - 314
Hauptverfasser:	Vo, An, Pham, Tan Ngoc, Nguyen, Van Bich, Luong, Ngoc Hoang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Ljubljana Slovenian Society Informatika / Slovensko drustvo Informatika 01.09.2023
Schlagworte:	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
Online-Zugang:	Volltext
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