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Genetic algorithms as a strategy for feature selection
Author(s) -
Leardi R.,
Boggia R.,
Terrile M.
Publication year - 1992
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180060506
Subject(s) - feature selection , selection (genetic algorithm) , feature (linguistics) , genetic algorithm , quality control and genetic algorithms , computer science , algorithm , simplex , simplex algorithm , artificial intelligence , machine learning , mathematical optimization , mathematics , meta optimization , linear programming , philosophy , linguistics , geometry
Genetic algorithms have been created as an optimization strategy to be used especially when complex response surfaces do not allow the use of better‐known methods (simplex, experimental design techniques, etc.). This paper shows that these algorithms, conveniently modified, can also be a valuable tool in solving the feature selection problem. The subsets of variables selected by genetic algorithms are generally more efficient than those obtained by classical methods of feature selection, since they can produce a better result by using a lower number of features.