RM-SAEA: Regularity Model Based Surrogate-Assisted Evolutionary Algorithms for Expensive Multi-Objective Optimization-Reference-Cited by-同舟云学术

RM-SAEA: Regularity Model Based Surrogate-Assisted Evolutionary Algorithms for Expensive Multi-Objective Optimization

Published:2023-07-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
language:
Short-container-title:

Author:

Lu Yongfan¹²^ORCID,Li Bingdong¹²^ORCID,Qian Hong¹²^ORCID,Hong Wenjing³^ORCID,Yang Peng³⁴^ORCID,Zhou Aimin¹²^ORCID

Affiliation:

1. Shanghai Institute of AI for Education, East China Normal University, Shanghai, China

2. School of Computer Science and Technology, East China Normal University, Shanghai, China

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

4. Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583131.3590435

Reference51 articles.

1. Víctor Adrián Sosa Hernández , Adriana Lara , Heike Trautmann , Günter Rudolph , and Oliver Schütze . 2016 . The directed search method for unconstrained parameter dependent multi-objective optimization problems . In NEO 2015: Results of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23--25 2015 in Tijuana, Mexico. Springer, 281--330 . Víctor Adrián Sosa Hernández, Adriana Lara, Heike Trautmann, Günter Rudolph, and Oliver Schütze. 2016. The directed search method for unconstrained parameter dependent multi-objective optimization problems. In NEO 2015: Results of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23--25 2015 in Tijuana, Mexico. Springer, 281--330.

2. Exploiting gradient information in numerical multi--objective evolutionary optimization

3. The balance between proximity and diversity in multiobjective evolutionary algorithms

4. Multi-objectivizing software configuration tuning

5. A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

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