Dual-State-Driven Evolutionary Optimization for Expensive Optimization Problems with Continuous and Categorical Variables-Reference-Cited by-同舟云学术

Dual-State-Driven Evolutionary Optimization for Expensive Optimization Problems with Continuous and Categorical Variables

Published:2023-09-22 Issue: Volume: Page:
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Container-title:2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS)
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Author:

Xie Lindong¹,Li Genghui¹,Lin Kangnian¹,Wang Zhenkun²

Affiliation:

1. School of System Design and Intelligent Manufacturing, Southern University of Science and Technology,Shenzhen,P.R. China

2. School of System Design and Intelligent Manufacturing, Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,P.R. China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10294323/10294356/10294894.pdf?arnumber=10294894

Reference30 articles.

1. Radial basis functions

2. Surrogate-assisted hybrid evolutionary algorithm with local estimation of distribution for expensive mixed-variable optimization problems

3. Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model

4. Radial Basis Function Assisted Optimization Method with Batch Infill Sampling Criterion for Expensive Optimization

5. A Surrogate-Assisted Multiswarm Optimization Algorithm for High-Dimensional Computationally Expensive Problems

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