  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.
