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告知下方采样词典选择:识别有效的培训案例,以有效解决问题
Ryan Boldi1, Martin Briesch2, Dominik Sobania3
1University of Massachusetts, Amherst, MA 01003, USA rbahlousbold@umass.edu.
Evolutionary computation
|January 25, 2024
概括
信息化下方样本词选择通过使用人口统计数据来选择更具信息性的培训案例来改进遗传编程 (GP). 这种方法在程序合成基准中显著优于随机抽样.
科学领域:
- 人工智能的人工智能
- 进化计算是一种进化计算.
背景情况:
- 遗传编程 (GP) 通常需要在广泛的训练数据集上评估所有个体.
- 随机下抽样的词汇选择提供了效率,但有可能排除关键的培训案例或过度使用同义词.
研究的目的:
- 引入和评估用于遗传编程的知情下方样本式lexicase选择.
- 通过利用人口统计数据来提高培训案例选择,以获得更有信息的下方样本.
主要方法:
- 开发了利用人口统计数据识别不同的培训案例的知情下方样本词典选择.
- 在程序合成基准上实证地研究了PushGP和语法指导GP系统中的方法.
主要成果:
- 在程序合成任务中,知情下方采样显著超过随机下方采样.
- 确保在整个进化运行和系统中始终包括重要的培训案例.
结论:
- 在全科医生中,知情下方采样式的lexicase选择 (Informed Down-Sampled Lexicase Selection) 提供了优于随机方法的性能.
- 这种方法很可能保持专家个体,同时降低评估成本,从而改善进化结果.
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