Messy Genetic Algorithms for Subset Feature Selection

Subset Feature Selection problems can have several attributes which may make Messy Ge netic Algorithms an appropriate optimization method First competitive solutions may of ten use only a small percentage of the total available features this can not only o er an advantage to Messy Genetic Algorithms it may also cause problems for other types of evolutionary algorithms Second the evalu ation of small blocks of features is naturally decomposable Thus there is no di culty evaluating underspeci ed strings We apply variants of the Messy Genetic Algorithm to a application in computer vision with very good results We also apply variants of the Fast Messy Genetic Algorithm to synthethic test problems

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