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Treatment approaches for psychological disorders fall into three main categories: psychological, biological, and sociocultural. Each approach targets different aspects of mental health, requiring varying levels of education and training.
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The optimal arousal theory suggests that performance is maximized when an individual experiences a moderate level of arousal. This theory is closely tied to the Yerkes-Dodson law, which illustrates an inverted U-shaped relationship between arousal and performance. The law, formulated by psychologists Robert Yerkes and John Dodson, implies an ideal arousal level for optimal performance, and deviations from this level can lead to declines in effectiveness.
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Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...
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MESPBO:用于全球优化问题和特征选择问题的多策略增强的基于学生心理学的优化算法.

Guolin Zhai1,2, Sai Li3,4

  • 1College of Tourism, Resources and Environment, Zaozhuang University, Zaozhuang 277160, China.

Biomimetics (Basel, Switzerland)
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概括
此摘要是机器生成的。

多策略增强的基于学生心理学的优化器 (MESPBO) 通过增强人口多样性和融合来改善特征选择和优化任务. 它在基准数据集和现实应用中显著优于现有的算法.

关键词:
功能选择 功能选择全球优化问题 全球优化问题的元启发式算法.基于学生心理学的优化算法.

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科学领域:

  • 机器学习 机器学习
  • 工程设计 工程设计 工程设计
  • 优化算法 优化算法

背景情况:

  • 功能选择和持续优化在机器学习和工程中至关重要但具有挑战性.
  • 现有的基于学生心理学的优化 (SPBO) 方法存在过早的融合和缺乏人口多样性的问题.

研究的目的:

  • 提出一个新的多策略增强的基于学生心理学的优化器 (MESPBO).
  • 在优化任务中解决过早融合和人口多样性不足的局限性.

主要方法:

  • 结合了使用拉丁语超立方样本和高斯扰动的混合启发式初始化.
  • 实施了一个适应式的双学习位置更新机制,用于勘探-开发平衡.
  • 利用基于混合对立的反射边界控制来增强搜索稳定性.

主要成果:

  • 在各种维度上,MESPBO在CEC2017基准套件上表现优于11个最先进的算法.
  • 在10个现实世界特征选择数据集中的9个中获得了最高的平均分类准确性,在一些数据集中达到100%.
  • 选择较小的特征子集 (通常是2-4个特征),不影响准确性,显示了比原始SPBO的10%的改进.

结论:

  • 在全球优化中,MESPBO表现出卓越的融合准确性,稳定性和可扩展性.
  • 拟议的方法在优化准确性和特征紧性之间提供了有效的权衡.
  • 对于全球优化和特征选择问题,MESPBO表现出强大的适应性和概括性.