Genetic subsets regression

Subset regression procedures have been shown to provide better overall performance than stepwise regression procedures. However, due to the combinatorial nature of evaluating each potential subset, subset regression techniques are costly to use. To resolve this difficulty, the use of a simple geneti...

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Bibliographic Details
Published in:Computers & industrial engineering 1996-09, Vol.30 (4), p.839-849
Main Authors: Sudjianto, Agus, Wasserman, Gary S., Sudarbo, Hinurimawan
Format: Article
Language:English
Subjects:
Algorithms
Industrial engineering
Regression analysis
Studies
Theory
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Summary:Subset regression procedures have been shown to provide better overall performance than stepwise regression procedures. However, due to the combinatorial nature of evaluating each potential subset, subset regression techniques are costly to use. To resolve this difficulty, the use of a simple genetic algorithm (GA) is proposed to reduce the number of subsets which must be evaluated. Any of a number of popular criteria, including Mallows' C p , MSE, R 2, AIC, etc., can be used to drive the search strategy associated with the use of the GA. Several illustrated examples on its use are provided.
ISSN:0360-8352
1879-0550
DOI:10.1016/0360-8352(95)00182-4