Algorithm selection based on exploratory landscape analysis and cost-sensitive learning

Author:

Bischl Bernd,Mersmann Olaf,Trautmann Heike,Preuß Mike

Publisher

ACM Press

Cited by 104 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2024-07-14

2. Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation;Proceedings of the Genetic and Evolutionary Computation Conference;2024-07-14

3. Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2024-07

4. MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts;ACM Transactions on Evolutionary Learning and Optimization;2024-06-21

5. CNN-HT: A Two-Stage Algorithm Selection Framework;Entropy;2024-03-14

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