Data-Driven Evolutionary Optimization: An Overview and Case Studies
By Yaochu Jin, Handing Wang, Tinkle Chugh, Dan Guo, Kaisa Miettinen. 2019
582 citations recorded by the source corpus.
Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
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Citations in the corpus
- Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, background): “Although new training data in online algorithms can significantly improve the quality of the model and optimization performance, offline algorithms are more practical than online algorithms due to the hardness and high cost in obtaining new data [40].”
- Recent advances and applications of surrogate models for finite element method computations: a review (2022, methodology): “Among the most frequently used used strategies of stationary sampling are the Latin Hypercube Sampling (LHS) (McKay et al. 1979), the maximin sampling (Johnson et al. 1990), and the sampling techniques of Morris and Mitchell (1995).”
- Multi-Objective Evolutionary Federated Learning (2018, methodology): “To address this issue, surrogate-assisted evolutionary optimization [29], [30] or Bayesian optimization [31] are helpful to reduce the computation cost.”