论文标题
使用解决方案和健身演化（安全）自动平衡模型的准确性和复杂性
Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE)
论文作者
论文摘要
在生物医学数据中寻求预测模型时，人们通常会想到一个不仅仅是一个目标，例如，达到高准确性和低复杂性（以促进可解释性）。我们在此研究我们最近提出的协同进化算法，安全（解决方案和健身进化）是否可以动态调整多个目标。我们发现，与配子工具产生的复杂模拟遗传数据集相比，与标准进化算法相比，安全能够自动调整精度和复杂性，而没有性能损失，而没有性能损失。
When seeking a predictive model in biomedical data, one often has more than a single objective in mind, e.g., attaining both high accuracy and low complexity (to promote interpretability). We investigate herein whether multiple objectives can be dynamically tuned by our recently proposed coevolutionary algorithm, SAFE (Solution And Fitness Evolution). We find that SAFE is able to automatically tune accuracy and complexity with no performance loss, as compared with a standard evolutionary algorithm, over complex simulated genetics datasets produced by the GAMETES tool.
