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. doi:10.1109/TEVC.2018.2869001
Published inIEEE Transactions on Evolutionary Computation
© 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. ...
PublisherInstitute of Electrical and Electronics Engineers
MetadataShow full item record
Showing items with similar title or keywords.
A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem Chugh, Tinkle; Chakraborti, Nirupam; Sindhya, Karthik; Jin, Yaochu (Taylor & Francis Inc., 2017)A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives ...
Nieminen, Paavo (University of Jyväskylä, 2016)Machine learning tasks usually come with several mutually conﬂicting objectives. One example is the simplicity of the learning device contrasted with the accuracy of its performance after learning. Another common example ...
Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm Chugh, Tinkle; Kratky, Tomas; Miettinen, Kaisa; Jin, Yaochu; Makkonen, Pekka (ACM, 2019)We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. ...
A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization Chugh, Tinkle; Jin, Yaochu; Miettinen, Kaisa; Hakanen, Jussi; Sindhya, Karthik (Institute of Electrical and Electronics Engineers, 2018)We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed ...
Laaksonen, Salla-Maaria; Haapoja, Jesse; Kinnunen, Teemu; Nelimarkka, Matti; Pöyhtäri, Reeta (Frontiers Media, 2020)Hate speech has been identified as a pressing problem in society and several automated approaches have been designed to detect and prevent it. This paper reports and reflects upon an action research setting consisting of ...