In the context of recommendation methods, meta-learning considers the use of previous knowledge regarding problems solution and performance to indicate the best strategy, whenever it faces a new similar problem. This paper studies the use of …
This work proposes a meta-learning system based on Gradient Boosting Machines to recommend local search heuristics for solving flowshop problems. The investigated approach can decide if a metaheuristic (MH) is suitable for each instance. It can also …
This work investigates the use of meta-learning for optimization tasks when a classic operational research problem (flowshop) is considered at the base level. It involves sequencing a set of jobs to be processed by machines in series aiming to …