Abstract

We present a collaboration between decision trees and an evolutionary algorithm inspired by Smart "Predict then Optimize" framework. In this work, we analyze the requirements for designing an evolutionary algorithm to solve complex combinatorial problem which can change over the time. We study the effect of changing the parameters of the objective function and how we can adapt the evolutionary algorithm to efficiently tackle new conditions of the problem. To predict the parameters of the objective function we use a decision tree. We evaluate our collaborative schema using instances for the shortest path problem and compare it with recently published work that uses complete techniques, obtaining encouraging results.