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Evolutionary Algorithms and Applications
TopicLeading institutions, researchers & key papers
This cluster of papers focuses on the application of genetic programming in machine learning, particularly in the areas of classification, feature selection, symbolic regression, and evolvable hardware. It explores the use of evolutionary algorithms and learning classifier systems to solve complex problems, with an emphasis on multiobjective optimization and semantic genetic programming.
80
Works
IDs:OpenAlex
How has Evolutionary Algorithms and Applications's publication output changed over time?
ScholarIQpublication output · 1998–2024
Output grew100% over the shown period — from 1 works in 1998 to 2 in 2024.
1
1
1
1
1
1
1
1
1
2
1998200420052006200820182019202120222024
What are the most-cited papers on Evolutionary Algorithms and Applications?
ScholarIQmost cited works
Prediction of surface roughness with genetic programming
M. Brezočnik, Miha Kovačić, Mirko Ficko
S127530304. 2004190 CitationsOPEN ACCESS
A Hybrid Genetic Algorithm With Wrapper-Embedded Approaches for Feature Selection
Xiaoying Liu, Yong Liang, Sai Wang, Ziyi Yang, Han-Shuo Ye
IEEE Access. 2018147 CitationsOPEN ACCESS
Chaos-Induced and Mutation-Driven Schemes Boosting Salp Chains-Inspired Optimizers
Qian Zhang, Huiling Chen, Ali Asghar Heidari, Xuehua Zhao, Yingying Xu, Pengjun Wang, Yuping Li, Chengye Li
IEEE Access. 2019110 CitationsOPEN ACCESS
Parallel Genetic Algorithms on Programmable Graphics Hardware
Qizhi Yu, Chongcheng Chen, Zhigeng Pan
Lecture notes in computer science. 200579 Citations
Where is Evolutionary Algorithms and Applications research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
Funder breakdown is a member featureSign up free to unlock
How much of the research on Evolutionary Algorithms and Applications is open access?
ScholarIQopen access share
25%OPEN ACCESS
Gold
17%
Green
8%
Hybrid
0%
Bronze
0%
Closed
75%
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