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.

翻译：在象征性回归任务中探索将相关性用作健身功能,并将绩效与典型的RMSE健身功能进行比较。利用相关性与匹配步骤来完成演进,使RMSE作为一种健身功能产生显著的绩效收益。将相关性作为健身功能,导致与RMSE相比,在几代人中找到解决方案。还发现,为发现正确的方程式,在培训中需要较少的数据点。在评估绩效时使用了Feynman符号回归基准以及其他一些旧的和近期的GP基准问题。