@InCollection{OliveiraLorePretStep:2004:AdHiFa,
               author = "Oliveira, Alexandre Cesar Muniz de and Lorena, Luiz Antonio 
                         Nogueira and Preto, Airam Jonatas and Stephany, Stephan",
                title = "An adaptive hierarchical fair competition genetic algorithm for 
                         large-scale numerical optimization",
            booktitle = "Proceedings of SBRN 2004 - 8th Brazilian Symposium on Neural 
                         Networks",
                 year = "2004",
               editor = "Barros, Allan and Araujo, Aluizio and Yehia, Hani C. and Teixeira, 
                         Roselito",
                pages = "x",
              address = "CA, USA",
             keywords = "genetic algorithms.",
             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 differences 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.",
          affiliation = "{Universidade Federal do Maranh{\~a}o (UFMA)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                 isbn = "8589029042",
             language = "en",
           targetfile = "oliveira_an adaptive.pdf",
        urlaccessdate = "02 maio 2024"
}