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Abstract
Divide-and-Conquer (DC) is conceptually well suited to deal with high-dimensional optimization problems by decomposing the original problem into multiple lowdimensional sub-problems, and tackling them separately. Nevertheless, the dimensionality mismatch between the original problem and sub-problems makes it non-trivial to precisely assess the quality of a candidate solution to a sub-problem, which has been a major hurdle for applying the idea of DC to non-separable highdimensional optimization problems. In this paper, we suggest that searching a good solution to a sub-problem can be viewed as a computationally expensive problem and can be addressed with the aid of meta-models. As a result, a novel approach, namely Self-Evaluation Evolution (SEE) is proposed. Empirical studies have shown the advantages of SEE over 4 representative compared algorithms increase with the problem size on the CEC2010 large scale global optimization benchmark. The weakness of SEE is also analysed in the empirical studies.
Original language | English |
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Journal | IEEE Transactions on Evolutionary Computation |
Early online date | 22 Feb 2017 |
DOIs | |
Publication status | E-pub ahead of print - 22 Feb 2017 |
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Dive into the research topics of 'Turning high-dimensional optimization into computationally expensive optimization'. Together they form a unique fingerprint.Projects
- 1 Finished
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Evolutionary Computation for Dynamic Optimisation in Network Environments
Engineering & Physical Science Research Council
25/02/13 → 17/08/17
Project: Research Councils