Comparing various regression methods on ensemble strategies in differential evolution
This is an incremental improvement for optimization algorithm users, focusing on strategy selection in differential evolution.
The paper tackled the problem of selecting the best strategy in differential evolution by using regression methods to predict strategies during optimization, and found that random forest regression outperformed other methods on a suite of five benchmark functions.
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.