Machine learning for improving performance in an evolutionary algorithm for minimum path with uncertain costs given by massively simulated scenarios
This work offers an incremental improvement in computational efficiency for researchers and practitioners dealing with robust optimization problems involving large-scale simulations.
This paper addresses the robust minimum-cost path problem in graphs with uncertain costs derived from numerous simulated scenarios. The authors used gradient boosting decision trees to classify candidate paths, forecasting costs for less promising candidates, which significantly improved computational performance with a limited loss of accuracy.
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.