A Primary Study on Hyper-Heuristics to Customise Metaheuristics for Continuous optimisation
Jorge M. Cruz‐Duarte, Iván Amaya, José Carlos Ortíz-Bayliss, Santiago Enrique Conant-Pablos, Hugo Terashima‐Marín
Abstract
Literature is prolific with metaheuristics for solving continuous optimisation problems. But, in practice, it is difficult to choose one appropriately. Moreover, it is necessary to determine a good enough set of parameters for the selected approach. Hence, this work proposes a strategy based on a hyper-heuristic for tailoring population-based metaheuristics. Besides, our approach considers search operators from well-known techniques as building blocks for new ones. We test this strategy through four benchmark functions and by varying their dimensions. We obtain metaheuristics with diverse configurations. We observe a possible performance boost when two or more search operators are considered. This could be due to previously unexplored interactions between such operators.