Automated design of local search algorithms: Predicting algorithmic components with LSTM

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 le...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Expert systems with applications Ročník 237; s. 121431
Hlavní autoři: Meng, Weiyao, Qu, Rong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.03.2024
Témata:
Automated algorithm design
Data re-sampling
Imbalanced dataset
LSTM
Search algorithms
Imbalanced dataset
LSTM
Automated algorithm design
Search algorithms
Data re-sampling
ISSN:0957-4174, 1873-6793
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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. •The design of local search algorithms is defined as a sequence classification task.•LSTM is applied to forecast algorithmic components for automated composition.•LSTM has a better classification performance as compared with other classifiers.•Key features for sequence classification are identified to support algorithm design.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.121431