Paper: Experience-Based Evolutionary Algorithms for Expensive Optimization. Authors: Xunzhao Yu, Yan Wang, Ling Zhu, Dimitar Filev, and Xin Yao.
Preprint of the main file and the supplementary material are available on arXiv: https://arxiv.org/abs/2304.04166.
This paper aims to use meta-learning method to enhance the efficiency of expensive optimization. A novel meta-learning method is developed, namely Meta Deep Kernel Learning (MDKL).
Code for Sinusoid regression experiments and DTLZ optimization experiments is included. Code for engine experiments is not included as we are not allowed to distribute them.