@InProceedings{OliveiraLorePretStep:2004:AdHiFa,
               author = "Oliveira, Alexandre Cesar Muniz and Lorena, Luiz Antonio Nogueira 
                         and Preto, Airam Jonatas and Stephani, Stephan",
          affiliation = "Federal University of Maranh{\~a}o, Department of Informatic and 
                         Universidade Federal do Maranh{\~a}o, Departamento de 
                         Inform{\'a}tica (UFMA) and National Institute for Space Research, 
                         Computing and Applied Mathematics Laboratory and Instituto 
                         Nacional de Pesquisas Espaciais, Laborat{\'o}rio de Associado de 
                         Computa{\c{c}}{\~a}o e Matem{\'a}tica Aplicada (INPE. LAC)",
                title = "An adaptive hierarchical fair competition genetic algorithm for 
                         large-scale numerical optimization",
            booktitle = "Proceedings...",
                 year = "2004",
                pages = "6",
         organization = "Brazilian Symposium in Neural Networks, 4. (SBRN).",
            publisher = "INPE",
             keywords = "COMPUTER SCIENCE, Genetic algorithms, Hierarchical Fair 
                         Competition (HFC), Computer systems performance, Message 
                         processing, Message Passing Interface (MPI), Optimization, 
                         COMPUTA{\C{C}}{\~A}O APLICADA, Algoritmos gen{\'e}ticos, 
                         Desempenho computacional, Processamento de mensagem, Interace de 
                         transmiss{\~a}o de mensagem, Competi{\c{c}}{\~a}o 
                         hierarquica.",
             abstract = "Genetic algorithms, inspired by the theory of evolution of 
                         species, are intended to be unfair. Individuals compete against 
                         each other and the best-adapted ones prevail. Unfairness is due to 
                         big dierences of skills, generally evaluated by a fitness measure, 
                         in a population of individuals competing for survival. However, 
                         population diversity is important to preserve some features that 
                         are not always associated to high ranked skills. Such diversity 
                         can be achieved by imposing fairness rules to the competition. The 
                         adaptive hierarchical fair competition genetic algorithm has been 
                         proposed to comply with this feature by segregating individuals in 
                         casts or demes, according to their fitness. This work proposes a 
                         parallel implementation that enhances the capabilities and 
                         computational performance of an adaptive hierarchical fair 
                         competition genetic algorithm. The code was parallelized using the 
                         MPI (Message Passing Interface) communication library and executed 
                         in a distributed memory parallel machine, a PC cluster. Test 
                         results are shown for standard numerical optimization problems 
                         presenting hundreds of variables.",
  conference-location = "S{\~a}o Luiz",
      conference-year = "29 Sept. - 01 Oct.",
           copyholder = "SID/SCD",
             language = "en",
                  ibi = "6qtX3pFwXQZsFDuKxG/EC3F7",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZsFDuKxG/EC3F7",
           targetfile = "fair.pdf",
        urlaccessdate = "02 maio 2024"
}