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@InProceedings{BarbosaSenn:2017:HeOpMe,
               author = "Barbosa, Eduardo Batista de Moraes and Senne, Edson Luiz 
                         Fran{\c{c}}a",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "A heuristic for optimization of metaheuristics by means of 
                         statistical methods",
            booktitle = "Proceedings...",
                 year = "2017",
                pages = "203--210",
         organization = "International Conference on Operations Research and Enterprise 
                         Systems, 6. (ICORES)",
             keywords = "Metaheuristics, Fine-tuning, Combinatorial optimization, 
                         Nonparametric statistics.",
             abstract = "The fine-tuning of the algorithms parameters, specially, in 
                         metaheuristics, is not always trivial and often is performed by ad 
                         hoc methods according to the problem under analysis. Usually, 
                         incorrect settings influence both in the algorithms performance, 
                         as in the quality of solutions. The tuning of metaheuristics 
                         requires the use of innovative methodologies, usually interesting 
                         to different research communities. In this context, this paper 
                         aims to contribute to the literature by presenting a methodology 
                         combining Statistical and Artificial Intelligence methods in the 
                         fine-tuning of metaheuristics. The key idea is a heuristic method, 
                         called Heuristic Oriented Racing Algorithm (HORA), which explores 
                         a search space of parameters, looking for candidate configurations 
                         near of a promising alternative, and consistently finds good 
                         settings for different metaheuristics. To confirm the validity of 
                         this approach, we present a case study for fine-tuning two 
                         distinct metaheuristics: Simulated Annealing (SA) and Genetic 
                         Algorithm (GA), in order to solve a classical task scheduling 
                         problem. The results of the proposed approach are compared with 
                         results yielded by the same metaheuristics tuned through different 
                         strategies, such as the brute-force and racing. Broadly, the 
                         proposed method proved to be effective in terms of the overall 
                         time of the tuning process. Our results from experimental studies 
                         reveal that metaheuristics tuned by means of HORA reach the same 
                         good results than when tuned by the other time-consuming 
                         fine-tuning approaches. Therefore, from the results presented in 
                         this study it is concluded that HORA is a promising and powerful 
                         tool for the fine-tuning of different metaheuristics, mainly when 
                         the overall time of tuning process is considered.",
  conference-location = "Porto, Portugal",
      conference-year = "23-25 Feb.",
                  doi = "10.5220/0006106402030210",
                  url = "http://dx.doi.org/10.5220/0006106402030210",
                 isbn = "978-989-758-218-9",
                label = "lattes: 8920905542032636 1 BarbosaSenn:2017:HeOpMe",
             language = "en",
           targetfile = "barbosa_a heuristic.pdf",
        urlaccessdate = "27 nov. 2020"
}


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