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English(EN) Lifelong Learning of Scheduling Rules with Surrogate-assisted Genetic Programming for Dynamic Flexible Job Shop Scheduling

新的遗传编程方法实现作业车间调度的终身学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的代理辅助遗传编程方法，以实现动态柔性作业车间调度（DFJSS）的终身学习。该方法旨在克服传统遗传编程的局限性，传统遗传编程在适应新任务或变化的作业特征时，常常会发生灾难性遗忘。所提出的框架平衡了可塑性和稳定性，根据任务相似性调节知识重用，并使用改进的具有距离约束的代理模型以获得更好的准确性。实验表明，该方法在保持适应性的同时有效减少了知识损失，在动态调度场景中优于现有技术。 AI

影响 这项研究可能导致动态环境中更具适应性和鲁棒性的调度系统，减少性能下降和知识损失。

排序理由 学术论文，详细介绍了用于调度中遗传编程终身学习的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的遗传编程方法实现作业车间调度的终身学习

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学术论文，详细介绍了用于调度中遗传编程终身学习的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Luyao Zhu, Fangfang Zhang, Yi Mei, Ke Chen, Mengjie Zhang ·

    利用代理辅助遗传编程实现调度规则的终身学习，用于动态柔性作业车间调度

    arXiv:2610.41158v1 Announce Type: new Abstract: Genetic programming (GP) has been widely used to learn scheduling rules for dynamic flexible job shop scheduling (DFJSS). However, trained rules tend to only generalise to tasks of similar scales and distributions, and when task cha…

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