Deep Reinforcement Learning for Smart Restarts in Exploration-Only Exploitation-Only Hybrid Metaheuristics
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Publisher
Springer Nature Switzerland
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https://link.springer.com/content/pdf/10.1007/978-3-031-62922-8_2
Reference17 articles.
1. Seyyedabbasi, A., Aliyev, R., Kiani, F., Gulle, M.U., Shah, M.A.: Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems. Knowl.-Based Syst. 223, 107044 (2021)
2. Bolufé-Röhler, A., Chen, S.: A multi-population exploration-only exploitation-only hybrid on cec-2020 single objective bound constrained problems. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)
3. Bolufé-Röhler, A., Tamayo-Vera, D.: an exploration-only exploitation-only hybrid for large scale global optimization. In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 1062–1069. IEEE (2021)
4. Bolufé-Röhler, A., Tamayo-Vera, D.: Machine learning based metaheuristic hybrids for S-box optimization. J. Ambient Intell. Humanized Comput. 11(11), 5139–5152 (2020). https://doi.org/10.1007/s12652-020-01829-y
5. Chen, S., Islam, S., Bolufé-Röhler, A., Montgomery, J., Hendtlass, T.: A random walk analysis of search in metaheuristics. In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 2323–2330. IEEE (2021)
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