Abstract
The real-world optimization task has long been viewed as a noteworthy challenge owing to its enormous search space. To deal with this challenge, evolution algorithm, especially differential evolution algorithm, attracts our attention owing to the excellent robustness. However, for traditional evolution algorithm, how to determine suitable parameters and strategies is a troublesome problem. To deal with the question the reinforced event-driven evolutionary algorithm (REDEA) based on double deep q-network is proposed which embed the double deep q-learning network into differential evolution algorithm with an event-driven controller. To verify the feasibility and superiority of our proposed algorithm, CEC 2013 test suits are utilized and four state-of-arts evolutionary algorithms are involved as the comparisons. The experimental results present that the proposal algorithm obtains comparable capability in most functions.
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Acknowledgement
The study was supported in part by the Natural Science Foundation of China Grant No. 62103286, No. 62001302, No. 71971143, in part by Social Science Youth Foundation of Ministry of Education of China under Grant 21YJC630181, in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515011348, 2019A1515111205, 2019A1515110401, in part by Natural Science Foundation of Guangdong Province under Grant 2020A1515010749, 2020A 1515010752, in part by Key Research Foundation of Higher Education of Guangdong Provincial Education Bureau under Grant 2019KZDXM030, in part by Natural Science Foundation of Shenzhen under Grant JCYJ20190808145011259, in part by Shenzhen Science and Technology Program under Grant RCBS2020071 4114920379, in part by Guangdong Province Innovation Team under Grant 2021WCXTD002.
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Zhou, T., Zhang, W., Lu, J., He, P., Yao, K. (2022). Reinforced Event-Driven Evolutionary Algorithm Based on Double Deep Q-network. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_25
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