Data-Driven Evolutionary Optimization : An Overview and Case Studies
Jin, Y., Wang, H., Chugh, T., Guo, D., & Miettinen, K. (2019). Data-Driven Evolutionary Optimization : An Overview and Case Studies. IEEE Transactions on Evolutionary Computation, 23(3), 442-458. https://doi.org/10.1109/TEVC.2018.2869001
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IEEE Transactions on Evolutionary ComputationDate
2019Copyright
© 2018 IEEE
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.
...


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Institute of Electrical and Electronics EngineersISSN Search the Publication Forum
1089-778XKeywords
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