Learning to Accelerate Evolutionary Search for Large-Scale Multiobjective Optimization-Reference-Cited by-同舟云学术

Learning to Accelerate Evolutionary Search for Large-Scale Multiobjective Optimization

Published:2023-02 Issue:1 Volume:27 Page:67-81
ISSN:1089-778X
Container-title:IEEE Transactions on Evolutionary Computation
language:
Short-container-title:IEEE Trans. Evol. Computat.

Author:

Liu Songbai¹^ORCID,Li Jun¹,Lin Qiuzhen¹^ORCID,Tian Ye²^ORCID,Tan Kay Chen³^ORCID

Affiliation:

1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

2. Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China

3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong

Funder

National Natural Science Foundation of China

Research Grants Council of the Hong Kong

Shenzhen Scientific Research and Development Funding Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/4235/10032250/09723458.pdf?arnumber=9723458

Reference58 articles.

1. Solution of Large-scale Many-objective Optimization Problems Based on Dimension Reduction and Solving Knowledge Guided Evolutionary Algorithm

2. PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]

3. Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses

4. A Fast Way of Calculating Exact Hypervolumes

5. A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization

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