Archive-Based Single-Objective Evolutionary Algorithms for Submodular Optimization
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70071-2_11
Reference15 articles.
1. Crawford, V.G.: Faster guarantees of evolutionary algorithms for maximization of monotone submodular functions. In: IJCAI, pp. 1661–1667. ijcai.org (2021)
2. Feige, U., Mirrokni, V.S., Vondrák, J.: Maximizing non-monotone submodular functions. SIAM J. Comput. 40(4), 1133–1153 (2011)
3. Friedrich, T., He, J., Hebbinghaus, N., Neumann, F., Witt, C.: Approximating covering problems by randomized search heuristics using multi-objective models. Evol. Comput. 18(4), 617–633 (2010)
4. Friedrich, T., Neumann, F.: Maximizing submodular functions under matroid constraints by evolutionary algorithms. Evol. Comput. 23(4), 543–558 (2015)
5. Giel, O.: Expected runtimes of a simple multi-objective evolutionary algorithm. In: Proceedings of CEC 2003, vol. 3, pp. 1918–1925 (2003). https://doi.org/10.1109/CEC.2003.1299908
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