Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary Algorithms-Reference-Cited by-同舟云学术

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Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary Algorithms

Published:2024-06-30 Issue: Volume: Page:01-08
ISSN:
Container-title:2024 IEEE Congress on Evolutionary Computation (CEC)
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Author:

Shu Tianye¹,Nan Yang¹,Shang Ke²,Ishibuchi Hisao¹

Affiliation:

1. Southern University of Science and Technology,Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,China,518055

2. Southern University of Science and Technology,Shenzhen,China,518055

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609966/10611750/10612171.pdf?arnumber=10612171

Reference34 articles.

1. Evolutionary multi-objective optimization: a historical view of the field

2. A fast and elitist multiobjective genetic algorithm: NSGA-II

3. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition

4. An EMO Algorithm Using the Hypervolume Measure as Selection Criterion

5. SMS-EMOA: Multiobjective selection based on dominated hypervolume

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