Interval-valued GA-P algorithms
Sanchez, L.
Dept. of Comput. Sci., Oviedo Univ.;
This paper appears in: Evolutionary Computation, IEEE Transactions on
Publication Date: Apr 2000
Volume: 4,
Issue: 1
On page(s): 64-72
ISSN: 1089-778X
References Cited: 24
CODEN: ITEVF5
INSPEC Accession Number: 6617602
Digital Object Identifier: 10.1109/4235.843495
Current Version Published: 2002-08-06
Abstract
When genetic programming (GP) methods are applied to solve
symbolic regression problems, we obtain a point estimate of a variable,
but it is not easy to calculate an associated confidence interval. We
designed an interval arithmetic-based model that solves this problem.
Our model extends a hybrid technique, the GA-P method, that combines
genetic algorithms and genetic programming. Models based on interval
GA-P can devise an interval model from examples and provide the
algebraic expression that best approximates the data. The method is
useful for generating a confidence interval for the output of a model,
and also for obtaining a robust point estimate from data which we know
to contain outliers. The algorithm was applied to a real problem related
to electrical energy distribution. Classical methods were applied first,
and then the interval GA-P. The results of both studies are used to
compare interval GA-P with GP, GA-P, classical regression methods,
neural networks, and fuzzy models
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
