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• W4386475652 abstract "With a recently defined AutoGCOP framework, the design of local search algorithms has been defined as the composition of elementary algorithmic components. The effective compositions of the best algorithms thus retain useful knowledge of effective algorithm design. This paper investigates machine learning to learn and extract useful knowledge in effective algorithmic compositions. The process of forecasting algorithmic components in the design of effective local search algorithms is defined as a sequence classification task, and solved by a long short-term memory (LSTM) neural network to systematically analyse algorithmic compositions. Compared with other learning models, the results reveal the superior prediction performance of the proposed LSTM. Further analysis identifies some key features of algorithmic compositions and confirms their effectiveness for improving the prediction, thus supporting effective automated algorithm design." @default.
• W4386475652 created "2023-09-07" @default.
• W4386475652 creator A5063920690 @default.
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• W4386475652 date "2024-03-01" @default.
• W4386475652 modified "2023-09-30" @default.
• W4386475652 title "Automated design of local search algorithms: Predicting algorithmic components with LSTM" @default.
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• W4386475652 doi "https://doi.org/10.1016/j.eswa.2023.121431" @default.
• W4386475652 hasPublicationYear "2024" @default.
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