A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments

Ke LI, Renzhi CHEN, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)


Many real-world problems are computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach to tackle expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. This paper proposes a simple yet effective transfer learning framework to empower data-driven evolutionary optimization to solve expensive dynamic optimization problems. Specifically, a hierarchical multi-output Gaussian process is proposed to capture the correlation among data collected from different time steps with a linearly increased number of hyperparameters. Furthermore, an adaptive source task selection along with a bespoke warm staring initialization mechanisms are proposed to better leverage the knowledge extracted from previous optimization processes. By doing so, the data-driven evolutionary optimization can jump start the optimization in the new environment with a very limited computational budget. Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm in comparison with nine state-of-the-art peer algorithms. IEEE
Original languageEnglish
Pages (from-to)1-1
Number of pages1
JournalIEEE Transactions on Evolutionary Computation
Publication statusPublished - 2023
Externally publishedYes


  • Closed box
  • Computational modeling
  • data-driven evolutionary optimization
  • dynamic optimization
  • Iron
  • kernel methods
  • Linear programming
  • Multi-ouput Gaussian processes
  • Optimization
  • Task analysis
  • transfer optimization
  • Uncertainty


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