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ICGA
1997

Messy Genetic Algorithms for Subset Feature Selection

13 years 11 months ago
Messy Genetic Algorithms for Subset Feature Selection
Subset Feature Selection problems can have severalattributes which may make Messy Genetic Algorithms an appropriateoptimization method. First, competitive solutions may often 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 evaluation 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.
L. Darrell Whitley, J. Ross Beveridge, Cesar Guerr
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1997
Where ICGA
Authors L. Darrell Whitley, J. Ross Beveridge, Cesar Guerra-Salcedo, Christopher R. Graves
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