Broderick CrawfordRicardo SotoEric MonfroyAstorga, GinoGinoAstorgaJosé GarcíaEnrique Cortes2025-12-062025-12-062017-01-019783319669625978331966963210.1007/978-3-319-66963-2_502-s2.0-85030031213https://cris-uv-2.scimago.es/handle/123456789/6919WOS:000449951100050In this paper, we solve the Set Covering Problem with a meta-optimization approach. One of the most popular models among facility location models is the Set Covering Problem. The meta-level metaheuristic operates on solutions representing the parameters of other metaheuristic. This approach is applied to an Artificial Bee Colony metaheuristic that solves the non-unicost set covering. The Artificial Bee Colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. This metaheuristic owns a parameter set with a great influence on the effectiveness of the search. These parameters are fine-tuned by a Genetic Algorithm, which trains the Artificial Bee Colony metaheuristic by using a portfolio of set covering problems. The experimental results show the effectiveness of our approach which produces very near optimal scores when solving set covering instances from the OR-Library.enacceso restringidoComputer ScienceMathematicsconference paper