Messy Genetic Algorithms for Subset Feature Selection.
L. Darrell Whitley, J. Ross Beveridge, César Guerra-Salcedo, Christopher Graves · 1997
Subset Feature Selection problems can have several attributes which may make Messy Genetic Algorithms an appropriate optimization method. First, competitive solutions may often use only a small percentage of the total available features; this can not only offer an advantage to Messy Genetic Algorithms, it may also cause problems for other types of evolutionary algorithms. Second, the evaluation of small blocks of features is naturally decomposable. Thus, there is no difficulty evaluating underspecified 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. Keywords: messy genetic algorithms, subset feature selection, computer vision 1 Subset Feature Selection The subset feature selection problem occurs in several domains, including machine learning and computer vision. In machine learning, many features may be available as potential in...