abstract
- © 2021 IEEE.Optimization is a field that never runs out nor becomes irrelevant. Nowadays, it is pretty hard to find a solver from the overpopulation of metaheuristics that properly deals with a given problem. This is even considered an additional problem. In this work, we propose a novel and simple methodology for solving the Metaheuristic Composition Optimization Problem, which involves designing heuristic-based procedures that solve continuous optimization problems. This methodology implements a naíve online learning that identifies the most relevant search operators to include in the candidate heuristic-based procedures. For representing these procedures, we adopt our previously proposed unfolded metaheuristic model. We prove the feasibility of this approach via a two-fold experiment employing several continuous optimization problems. Our data revealed that the learning procedure is worthwhile, finding adequate solutions for problems in up to 50 dimensions.