  In this work we introduce an implementation for which machine learning
techniques helped improve the overall performance of an evolutionary algorithm
for an optimization problem, namely a variation of robust minimum-cost path in
graphs. In this big data optimization problem, a path achieving a good cost in
most scenarios from an available set of scenarios (generated by a simulation
process) must be obtained. The most expensive task of our evolutionary
algorithm, in terms of computational resources, is the evaluation of candidate
paths: the fitness function must calculate the cost of the candidate path in
every generated scenario. Given the large number of scenarios, this task must
be implemented in a distributed environment. We implemented gradient boosting
decision trees to classify candidate paths in order to identify good
candidates. The cost of the not-so-good candidates is simply forecasted. We
studied the training process, gain performance, accuracy, and other variables.
Our computational experiments show that the computational performance was
significantly improved at the expense of a limited loss of accuracy.
