Optimal Experiment Design for Coevolutionary Active Learning
Optimal Experiment Design for Coevolutionary Active Learning
复制标题
协同进化主动学习的最优实验设计
DOI：
10.1109/tevc.2013.2281529
复制
发表时间：
2014
影响因子：
14.3
通讯作者：
Hod Lipson
中科院分区：
文献类型：
--
作者：
D. Ly;Hod Lipson
本文提出了一种策略，选择最信息的个人在教师-学习者型的共同进化。我们建议使用基于香农信息理论的平均值，最好的消除歧义的任意和竞争模型的集合，仅仅基于他们的预测。这个政策是证明在一个迭代的共同进化框架内，由符号回归模型推理和遗传算法的最优实验设计。使用少于32个观测值可靠地推断复杂的符号表达式。与基线相比，该策略需要的模型推理实验减少了21%，并且在存在噪声破坏，局部信息内容以及高维系统的情况下特别有效。此外，该策略在现实环境中应用于混凝土抗压强度建模，仅用16.6%的数据就能实现96.1%的被动机器学习基线性能。
This paper presents a policy for selecting the most informative individuals in a teacher-learner type coevolution. We propose the use of the surprisal of the mean, based on Shannon information theory, which best disambiguates a collection of arbitrary and competing models based solely on their predictions. This policy is demonstrated within an iterative coevolutionary framework consisting of symbolic regression for model inference and a genetic algorithm for optimal experiment design. Complex symbolic expressions are reliably inferred using fewer than 32 observations. The policy requires 21% fewer experiments for model inference compared to the baselines and is particularly effective in the presence of noise corruption, local information content as well as high dimensional systems. Furthermore, the policy was applied in a real-world setting to model concrete compression strength, where it was able to achieve 96.1% of the passive machine learning baseline performance with only 16.6% of the data.