Abstract
Recently, evolutionary multi-tasking (EMT) has been proposed as a new search paradigm for optimizing multiple problems simultaneously. Since the beneficial knowledge can be transferred among tasks to speed up the optimization process, EMT shows better performance in many problems compared with single-task evolutionary search algorithms. Notably, existing works on EMT have been devoted to the evolutionary search with two tasks. However, when multiple tasks (i.e., number of tasks >2) are involved, the existing methods might fail as it is necessary to decide which is the most suitable task to be selected for performing the knowledge transfer. To address this issue, we propose an online credit information based task selection algorithm to enhance the performance of EMT. Specifically, a credit matrix is introduced to express the transfer qualities among tasks. Then, we design two kinds of credit information and propose an updating mechanism to adjust the credit matrix online. After that, a greedy task selection mechanism is proposed for balancing the exploration and exploitation of the task selection process. Besides, we also propose an adaptive transfer rate to enhance positive transfer while reduce the impact of negative transfer. In the experiment, we compare our method with the existing works. The results clearly demonstrate the efficacy of the proposed method.
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0910500, in part by the Liaoning Key R&D Program under Grant 2019JH210100030, in part by the Liaoning United Foundation under Grant U1908214, in part by the National Natural Science Foundation of China under Grant 61906032, and in part by the Fundamental Research Funds for the Central Universities under Grant DUT18RC(3)069.
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Cao, Y., Hou, Y., Feng, L., Ge, H., Zhang, Q., Wei, X. (2021). A Study on Realtime Task Selection Based on Credit Information Updating in Evolutionary Multitasking. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_38
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