Novel Decomposition-Based Multi-objective Evolutionary Algorithm Using Reinforcement Learning Adaptive Operator Selection (MOEA/D-QL)
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-55684-5_11
Reference13 articles.
1. Sun, L., Li, K.: Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D (Version 1). arXiv (2020)
2. Li, K., Fialho, A., Kwong, S., Zhang, Q.: Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 18(1), 114–130 (2014). Institute of Electrical and Electronics Engineers (IEEE)
3. Fialho, Á., da Costa, L., Schoenauer, M., Sebag, M.: Analyzing bandit-based adaptive operator selection mechanisms. Annals Math. Artif. Intell. 60(1), 25–64 (2010). Springer Verlag
4. Goldberg, D.E.: Probability matching, the magnitude of reinforcement, and classifier system bidding. Mach. Learn. 5(4), 407–425 (1990). Springer Science and Business Media LLC
5. Thierens, D.: An adaptive pursuit strategy for allocating operator probabilities. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation—GECCO ’05. The 2005 Conference. ACM Press (2005)
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