Multi-Task Surrogate-Assisted Search with Bayesian Competitive Knowledge Transfer for Expensive Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | Lu, Yi, Xue, Xiaoming, Zhang, Kai, Zhang, Liming, Chen, Guodong, Cao, Chenming, Liu, Piyang, Tan, Kay Chen |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Surrogate-Assisted Search with Competitive Knowledge Transfer for Expensive Optimization
by: Xue, Xiaoming, et al.
Published: (2024)
by: Xue, Xiaoming, et al.
Published: (2024)
A RankNet-Inspired Surrogate-Assisted Hybrid Metaheuristic for Expensive Coverage Optimization
by: Wu, Tongyu, et al.
Published: (2025)
by: Wu, Tongyu, et al.
Published: (2025)
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning
by: Zhang, Huan, et al.
Published: (2023)
by: Zhang, Huan, et al.
Published: (2023)
A Theoretical Analysis of Analogy-Based Evolutionary Transfer Optimization
by: Xue, Xiaoming, et al.
Published: (2025)
by: Xue, Xiaoming, et al.
Published: (2025)
Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems
by: Chen, Guodong, et al.
Published: (2023)
by: Chen, Guodong, et al.
Published: (2023)
Large Language Model-Driven Surrogate-Assisted Evolutionary Algorithm for Expensive Optimization
by: Xie, Lindong, et al.
Published: (2025)
by: Xie, Lindong, et al.
Published: (2025)
Parametric Pareto Set Learning for Expensive Multi-Objective Optimization
by: Cheng, Ji, et al.
Published: (2025)
by: Cheng, Ji, et al.
Published: (2025)
Un-evaluated Solutions May Be Valuable in Expensive Optimization
by: Hao, Hao, et al.
Published: (2024)
by: Hao, Hao, et al.
Published: (2024)
Towards Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models
by: Lyu, Xuebin, et al.
Published: (2024)
by: Lyu, Xuebin, et al.
Published: (2024)
Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation
by: Rodriguez, C. J., et al.
Published: (2024)
by: Rodriguez, C. J., et al.
Published: (2024)
Surrogate Ensemble in Expensive Multi-Objective Optimization via Deep Q-Learning
by: Wu, Yuxin, et al.
Published: (2026)
by: Wu, Yuxin, et al.
Published: (2026)
Relation Reasoning with LLMs in Expensive Optimization
by: Lu, Ye, et al.
Published: (2026)
by: Lu, Ye, et al.
Published: (2026)
Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization
by: Wang, Zeyi, et al.
Published: (2024)
by: Wang, Zeyi, et al.
Published: (2024)
Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization
by: Du, Yukun, et al.
Published: (2026)
by: Du, Yukun, et al.
Published: (2026)
Island-Based Evolutionary Computation with Diverse Surrogates and Adaptive Knowledge Transfer for High-Dimensional Data-Driven Optimization
by: Zhang, Xian-Rong, et al.
Published: (2025)
by: Zhang, Xian-Rong, et al.
Published: (2025)
An Efficient Approach for Solving Expensive Constrained Multiobjective Optimization Problems
by: Rahi, Kamrul Hasan
Published: (2024)
by: Rahi, Kamrul Hasan
Published: (2024)
Energy and Quality of Surrogate-Assisted Search Algorithms: a First Analysis
by: Harada, Tomohiro, et al.
Published: (2025)
by: Harada, Tomohiro, et al.
Published: (2025)
Composite Indicator-Guided Infilling Sampling for Expensive Multi-Objective Optimization
by: Zhen, Huixiang, et al.
Published: (2025)
by: Zhen, Huixiang, et al.
Published: (2025)
Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization
by: Zhong, Rui, et al.
Published: (2024)
by: Zhong, Rui, et al.
Published: (2024)
Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding
by: Zhang, Yulun, et al.
Published: (2021)
by: Zhang, Yulun, et al.
Published: (2021)
Experience-Based Evolutionary Algorithms for Expensive Optimization
by: Yu, Xunzhao, et al.
Published: (2023)
by: Yu, Xunzhao, et al.
Published: (2023)
A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
by: Zhang, Yisong, et al.
Published: (2025)
by: Zhang, Yisong, et al.
Published: (2025)
Autonomous Multi-Objective Optimization Using Large Language Model
by: Huang, Yuxiao, et al.
Published: (2024)
by: Huang, Yuxiao, et al.
Published: (2024)
Learning to Evolve for Optimization via Stability-Inducing Neural Unrolling
by: Gao, Jiaxin, et al.
Published: (2025)
by: Gao, Jiaxin, et al.
Published: (2025)
Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization
by: Liu, Jiao, et al.
Published: (2023)
by: Liu, Jiao, et al.
Published: (2023)
Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training
by: Hong, Haokai, et al.
Published: (2023)
by: Hong, Haokai, et al.
Published: (2023)
Learning to Transfer for Evolutionary Multitasking
by: Wu, Sheng-Hao, et al.
Published: (2024)
by: Wu, Sheng-Hao, et al.
Published: (2024)
Impact of Surrogate Model Accuracy on Performance and Model Management Strategy in Surrogate-Assisted Evolutionary Algorithms
by: Hanawa, Yuki, et al.
Published: (2025)
by: Hanawa, Yuki, et al.
Published: (2025)
Reinforcement Learning-assisted Constraint Relaxation for Constrained Expensive Optimization
by: Zhu, Qianhao, et al.
Published: (2026)
by: Zhu, Qianhao, et al.
Published: (2026)
EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
by: Bliek, Laurens, et al.
Published: (2021)
by: Bliek, Laurens, et al.
Published: (2021)
In Search of Excellence: SHOA as a Competitive Shrike Optimization Algorithm for Multimodal Problems
by: AbdulKarim, Hanan K., et al.
Published: (2024)
by: AbdulKarim, Hanan K., et al.
Published: (2024)
Surrogate-Assisted Genetic Programming with Rank-Based Phenotypic Characterisation for Dynamic Multi-Mode Project Scheduling
by: Tian, Yuan, et al.
Published: (2026)
by: Tian, Yuan, et al.
Published: (2026)
Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search
by: Somu, Pranav, et al.
Published: (2026)
by: Somu, Pranav, et al.
Published: (2026)
COBRA++: Enhanced COBRA Optimizer with Augmented Surrogate Pool and Reinforced Surrogate Selection
by: Yu, Zipei, et al.
Published: (2026)
by: Yu, Zipei, et al.
Published: (2026)
Evolutionary Generative Optimization: Towards Fully Data-Driven Evolutionary Optimization via Generative Learning
by: Jiang, Tao, et al.
Published: (2025)
by: Jiang, Tao, et al.
Published: (2025)
Large Language Model Assisted Adversarial Robustness Neural Architecture Search
by: Zhong, Rui, et al.
Published: (2024)
by: Zhong, Rui, et al.
Published: (2024)
Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Study
by: Ma, Zeyuan, et al.
Published: (2025)
by: Ma, Zeyuan, et al.
Published: (2025)
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
by: Zheng, Bowen, et al.
Published: (2025)
by: Zheng, Bowen, et al.
Published: (2025)
A New Scope and Domain Measure Comparison Method for Global Convergence Analysis in Evolutionary Computation
by: Luo, Liu-Yue, et al.
Published: (2025)
by: Luo, Liu-Yue, et al.
Published: (2025)
Adaptive Surrogate-Based Strategy for Accelerating Convergence Speed when Solving Expensive Unconstrained Multi-Objective Optimisation Problems
by: Banda, Tiwonge Msulira, et al.
Published: (2026)
by: Banda, Tiwonge Msulira, et al.
Published: (2026)
Similar Items
-
Surrogate-Assisted Search with Competitive Knowledge Transfer for Expensive Optimization
by: Xue, Xiaoming, et al.
Published: (2024) -
A RankNet-Inspired Surrogate-Assisted Hybrid Metaheuristic for Expensive Coverage Optimization
by: Wu, Tongyu, et al.
Published: (2025) -
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning
by: Zhang, Huan, et al.
Published: (2023) -
A Theoretical Analysis of Analogy-Based Evolutionary Transfer Optimization
by: Xue, Xiaoming, et al.
Published: (2025) -
Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems
by: Chen, Guodong, et al.
Published: (2023)