论文标题
符号回归任务中的相关性与RMSE损失函数
Correlation versus RMSE Loss Functions in Symbolic Regression Tasks
论文作者
论文摘要
在符号回归任务中探索了相关性作为健身函数的使用，并将性能与典型的RMSE健身函数进行了比较。使用与对齐步骤的相关性来结论演变导致RMSE作为适应性函数的显着性能提高。与RMSE相比，使用相关性作为健身函数导致了较少世代的解决方案，并且发现在训练集中需要更少的数据点才能发现正确的方程。 Feynman符号回归基准以及其他几个旧的和最近的GP基准问题用于评估性能。
The use of correlation as a fitness function is explored in symbolic regression tasks and the performance is compared against the typical RMSE fitness function. Using correlation with an alignment step to conclude the evolution led to significant performance gains over RMSE as a fitness function. Using correlation as a fitness function led to solutions being found in fewer generations compared to RMSE, as well it was found that fewer data points were needed in the training set to discover the correct equations. The Feynman Symbolic Regression Benchmark as well as several other old and recent GP benchmark problems were used to evaluate performance.
