Many real-world complex optimization problems can be solved based on data only, which is known as data-driven optimization. In this talk, we discuss the main challenges in data-driven evolutionary algorithms resulting from complexities in data as well the problems to be optimized. We then present recent advances in data-driven optimization that systematically integrate advanced machine learning techniques including active learning, semi-supervised learning and transfer learning, with evolutionary algorithms. Real-world examples are provided to illustrate different model management strategies for handing different data-driven optimization problems. [Go to the full record in the library's catalogue]
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