A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 27; no. 6; pp. 1941 - 1961
Main Authors:	Liu, Songbai, Lin, Qiuzhen, Li, Jianqiang, Tan, Kay Chen
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Evolution (biology) Evolutionary algorithms Evolutionary computation Generators Genetic algorithms Learnable evolutionary algorithms Machine learning machine learning (ML) Multiple objective analysis Optimization scalable multiobjective optimization Scaling Search problems Sociology Statistics Taxonomy
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!