Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Evolutionary design of neural network architectures: a review of three decades of research
Artificial Intelligence Review · Mart 2022
YÖKSİS Kayıtları
Evolutionary design of neural network architectures: a review of three decades of research
Artificial Intelligence Review · 2022 SCI-Expanded
Prof. Dr. FATİH BAŞÇİFTÇİ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Evolutionary design of neural network architectures: a review of three decades of research
2022 ISSN: 0269-2821 SCI-Expanded Q1
Prof. Dr. FATİH BAŞÇİFTÇİ →
A novel adaptive memetic binary optimization algorithm for feature selection
2023 ISSN: 0269-2821 SCI-Expanded Q1
Doç. Dr. AHMET CEVAHİR ÇINAR →
Makale Bilgileri
ISSN02692821
Yayın TarihiMart 2022
Cilt / Sayfa55 · 1723-1802
Scopus ID2-s2.0-85111520289
Özet
We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.
Yazarlar (2)
1
Hamit Taner Ünal
ORCID: 0000-0002-9397-8862
2
Fatih Başçiftçi
Anahtar Kelimeler
Artificial intelligence
Artificial neural networks
Evolutionary computation
Machine learning
Optimization
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Artificial Intelligence Review
Q1
SJR Skoru3,638
H-Index169
YayıncıSpringer Netherlands
ÜlkeNetherlands
Artificial Intelligence (Q1)
Linguistics and Language (Q1)
Metrikler
77
Atıf
2
Yazar
5
Anahtar Kelime