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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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基因编程与塔布列表用于动态灵活的工作车间调度

Fangfang Zhang1, Mazhar Ansari Ardeh2, Yi Mei3

  • 1Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand fangfang.zhang@ecs.vuw.ac.nz.

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概括
此摘要是机器生成的。

本研究介绍了一种基因编程 (GP) 算法,使用 tabu 列表来增强动态灵活工作室调度 (DFJSS) 的探索. 改进的GP有效地保持了人口多样性,并发现了更好的调度启发式.

关键词:
动态灵活的工作车间安排.探索能力 探索能力基因编程是一种基因编程.调度启发式调度时间表塔布列表 塔布列表 塔布列表

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科学领域:

  • 运营研究 运营研究
  • 人工智能的人工智能
  • 计算机科学 计算机科学

背景情况:

  • 动态灵活的工作车间调度 (DFJSS) 是一个复杂的组合优化问题.
  • 遗传编程 (GP) 是DFJSS的一个常见的超启发方法,但遭受了快速的多样性丧失.
  • 软弱的探索能力限制了GP在寻找最佳调度启发技巧方面的有效性.

研究的目的:

  • 提出一个有效的GP算法与禁忌列表来增强DFJSS的探索.
  • 通过引导GP进入尚未探索的地区来提高GP的探索能力.
  • 提高GP解决DFJSS问题的整体有效性.

主要方法:

  • 利用表型特征来代表GP个体作为DFJSS的载体.
  • 开发了禁忌清单,以存储探索的个体的表型特征.
  • 实施了一种机制,如果在禁忌清单中发现它们的表型特征,可以丢弃后代,从而促进探索未见的解决方案.

主要成果:

  • 建议使用 tabu 列表的 GP 算法在大多数测试场景中表现优于比较算法.
  • 该算法在整个进化过程中成功地保持了多样化和分布良好的人口.
  • 证明该算法探索更大的搜索空间以识别有效的调度启发式.

结论:

  • 建议使用 tabu 列表的 GP 算法在增强 DFJSS 的探索方面是有效的.
  • 该方法提高了人口的多样性,并导致发现了优越的调度启发式.
  • 这种方法为改善动态调度环境中的超启发性性能提供了一个有希望的方向.