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Expensive Optimization via Relation
  • Hao Hao,
  • Xiaoqun Zhang,
  • Aimin Zhou
Hao Hao

Corresponding Author:[email protected]

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Xiaoqun Zhang
Aimin Zhou

Corresponding Author:

Abstract

Expensive optimization problems pose significant challenges to traditional gradient-free optimization due to their costly evaluation overhead. Surrogate model-assisted evolutionary optimization, which substitutes expensive evaluation functions with surrogate models, can effectively overcome these challenges. However, designing an efficient surrogate model is the key issue in model-assisted evolutionary optimization. In recent years, establishing surrogate models through the relationships between solutions has become a promising modeling strategy, following regression and classification models. Yet, there has been no systematic organization or summary of relation models. Therefore, this paper views the relation as a perspective to outline the development context of this field, defines the framework for researching relation models, and reviews typical strategies under each framework. Finally, it validates the effectiveness of numerous strategies through experiments. The entire strategy collection will be open-sourced on GitHub , making it easier for more researchers to participate in this field of study.
14 May 2024Submitted to TechRxiv
20 May 2024Published in TechRxiv