论文标题
空间应用的符号回归：由多目标模因算法提供支持的笛卡尔遗传编程
Symbolic Regression for Space Applications: Differentiable Cartesian Genetic Programming Powered by Multi-objective Memetic Algorithms
论文作者
论文摘要
可解释的回归模型对于许多应用程序领域都很重要，因为它们允许专家从稀疏数据中了解变量之间的关系。符号回归通过搜索可以从基本代数函数构建的所有可能的自由形式方程的空间来解决此问题。尽管可以通过这种方式重新发现明确的数学函数，但在搜索过程中确定未知数值常数一直是一个经常被忽略的问题。我们提出了一种新的多目标模因算法，该算法利用了一个可区分的笛卡尔遗传编程编码，以在进化循环期间学习常数。我们表明，这种方法具有竞争力或胜过机器的黑匣子回归模型或用于两个应用的手工设计拟合：Mars Express Texpress热力估计和通过陀螺的测定恒星的确定。
Interpretable regression models are important for many application domains, as they allow experts to understand relations between variables from sparse data. Symbolic regression addresses this issue by searching the space of all possible free form equations that can be constructed from elementary algebraic functions. While explicit mathematical functions can be rediscovered this way, the determination of unknown numerical constants during search has been an often neglected issue. We propose a new multi-objective memetic algorithm that exploits a differentiable Cartesian Genetic Programming encoding to learn constants during evolutionary loops. We show that this approach is competitive or outperforms machine learned black box regression models or hand-engineered fits for two applications from space: the Mars express thermal power estimation and the determination of the age of stars by gyrochronology.
