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Efficient use of partially converged simulations in evolutionary optimization
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Branke, Jürgen, Asafuddoula, M., Bhattacharjee, Kalyan Shankar and Ray, Tapabrata (2017) Efficient use of partially converged simulations in evolutionary optimization. IEEE Transactions on Evolutionary Computation, 21 (1). pp. 52-64. doi:10.1109/TEVC.2016.2569018 ISSN 1089-778X.
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Official URL: https://doi.org/10.1109/TEVC.2016.2569018
Abstract
For many real-world optimization problems, evaluating a solution involves running a computationally expensive simulation model. This makes it challenging to use evolutionary algorithms which usually have to evaluate thousands of solutions before converging. On the other hand, in many cases, even a prematurely stopped run of the simulation may serve as a cheaper, albeit less accurate (low fidelity), estimate of the true fitness value. For evolutionary optimization, this opens up the opportunity to decide about the simulation run length for each individual. In this paper, we propose a mechanism that is capable of learning the appropriate simulation run length for each solution. To test our approach, we propose two new benchmark problems, one simple artificial benchmark function and one benchmark based on a computational fluid dynamics simulation scenario to design a toy submarine. As we demonstrate, our proposed algorithm finds good solutions much faster than always using the full computational fluid dynamics simulation and provides much better solution quality than a strategy of progressively increasing the fidelity level over the course of optimization.
Item Type: | Journal Article | ||||||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||||
Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||||
Library of Congress Subject Headings (LCSH): | Mathematical optimization -- Technology -- Research, Computational fluid dynamics -- Mathematical models, Algorithms | ||||||||
Journal or Publication Title: | IEEE Transactions on Evolutionary Computation | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 1089-778X | ||||||||
Official Date: | February 2017 | ||||||||
Dates: |
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Volume: | 21 | ||||||||
Number: | 1 | ||||||||
Number of Pages: | 13 | ||||||||
Page Range: | pp. 52-64 | ||||||||
DOI: | 10.1109/TEVC.2016.2569018 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 13 May 2016 | ||||||||
Date of first compliant Open Access: | 16 May 2016 | ||||||||
Funder: | Australian Research Council (ARC), University of New South Wales (UNSW) |
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