  Differential evolution possesses a multitude of various strategies for
generating new trial solutions. Unfortunately, the best strategy is not known
in advance. Moreover, this strategy usually depends on the problem to be
solved. This paper suggests using various regression methods (like random
forest, extremely randomized trees, gradient boosting, decision trees, and a
generalized linear model) on ensemble strategies in differential evolution
algorithm by predicting the best differential evolution strategy during the
run. Comparing the preliminary results of this algorithm by optimizing a suite
of five well-known functions from literature, it was shown that using the
random forest regression method substantially outperformed the results of the
other regression methods.
