Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/325730 
Year of Publication: 
2022
Citation: 
[Journal:] Operations Research Perspectives [ISSN:] 2214-7160 [Volume:] 9 [Article No.:] 100237 [Year:] 2022 [Pages:] 1-10
Publisher: 
Elsevier, Amsterdam
Abstract: 
Seismic History Matching (SHM) is a key problem in the geosciences community, requiring optimal parameters of a subsurface model that match the observed data from multiple in-situ measurements. Therefore, the SHM problems are usually solved with Multi-Objective Evolutionary Algorithms (MOEAs). This group of algorithms optimize multiple objectives simultaneously, considering the trade-off between objectives. However, SHM requires the solutions that are good on all objectives rather than a trade-off. In this study, we propose a Differential Evolution algorithm using Lexicase Selection to solve the SHM problems. Unlike the MOEAs, this selection method pushes the solutions to perform well on all objectives. We compared this method with two MOEAs, namely Non-dominated Sorting Genetic Algorithm II and Reference Vector-guided Evolutionary Algorithm, on two SHM problems. The results show that this method generates more solutions near the ground truth.
Subjects: 
Lexicase Selection
Multi-Objective Evolutionary Algorithm
Multi-objective optimization
Seismic History Matching
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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