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• W4313256881 abstract "Some real-world design optimization problems can be formulated as expensive mixed-variable optimization problems (EMVOPs), which involve both continuous and discrete decision variables and expensive function evaluations. The main challenges for solving EMVOPs are the handling of mixed variables, limited number of function evaluations and multiple disconnected regions in the search space. In this work, we propose a novel algorithm with global and local search strategies for improving the search ability on disconnected regions, and it only consumes hundreds of function evaluations. The global module employs hybrid evolutionary operators and a Gower distance based surrogate model for handling mixed variables. The local estimation of distributions in different local regions performs in a competitive switching way to combine their advantages, and local surrogate models trained with selected samples improve the accuracy of approximated evaluations for the locally generated solutions. In the late stage, a local continuous search module is executed for refining the continuous decision variables. Verification results on the artificial benchmarks demonstrate that our proposed algorithm is competitive and works effectively. To verify the practicability of the algorithm, it is applied on a convolutional neural network hyperparameter optimization problems and obtains satisfactory results." @default.
• W4313256881 created "2023-01-06" @default.
• W4313256881 creator A5027677425 @default.
• W4313256881 creator A5091324372 @default.
• W4313256881 date "2023-01-01" @default.
• W4313256881 modified "2023-09-26" @default.
• W4313256881 title "Surrogate-assisted hybrid evolutionary algorithm with local estimation of distribution for expensive mixed-variable optimization problems" @default.
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• W4313256881 doi "https://doi.org/10.1016/j.asoc.2022.109957" @default.
• W4313256881 hasPublicationYear "2023" @default.
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