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@Article{BarbosaSenn:2017:ApCoDe,
               author = "Barbosa, Eduardo Batista de Moraes and Senne, Edson Luiz 
                         Fran{\c{c}}a",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Estadual Paulista (UNESP)}",
                title = "Improving the fine-tuning of metaheuristics: an approach combining 
                         design of experiments and racing algorithms",
              journal = "Journal of Optimization",
                 year = "2017",
               volume = "2017",
                pages = "1--7",
             keywords = "Metaheuristics, Fine-tuning, Combinatorial optimization, 
                         Nonparametric statistics.",
             abstract = "Usually, metaheuristic algorithms are adapted to a large set of 
                         problems by applying few modifications on parameters for each 
                         specific case. However, this flexibility demands a huge effort to 
                         correctly tune such parameters. Therefore, the tuning of 
                         metaheuristics arises as one of the most important challenges in 
                         the context of research of these algorithms.Thus, this paper aims 
                         to present a methodology combining Statistical andArtificial 
                         Intelligencemethods in the fine-tuning ofmetaheuristics.Thekey 
                         idea is a heuristic method, called Heuristic Oriented Racing 
                         Algorithm (HORA), which explores a search space of parameters 
                         looking for candidate configurations close to a promising 
                         alternative. To confirm the validity of this approach, we present 
                         a case study for finetuning two distinct metaheuristics: Simulated 
                         Annealing (SA) and Genetic Algorithm (GA), in order to solve the 
                         classical traveling salesman problem. The results are compared 
                         considering the same metaheuristics tuned through a racing method. 
                         Broadly, the proposed approach proved to be effective in terms of 
                         the overall time of the tuning process. Our results reveal that 
                         metaheuristics tuned by means of HORA achieve, with much less 
                         computational effort, similar results compared to the case when 
                         they are tuned by the other fine-tuning approach.",
                  doi = "10.1155/2017/8042436",
                  url = "http://dx.doi.org/10.1155/2017/8042436",
                 issn = "2356-752X",
                label = "lattes: 8920905542032636 1 BarbosaSenn:2017:ApCoDe",
             language = "en",
           targetfile = "barbosa_improving.pdf",
        urlaccessdate = "30 nov. 2020"
}


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