Interval-valued GA-P algorithms
Sanchez, L.
Evolutionary Computation, IEEE Transactions on
Volume 4, Issue 1, Apr 2000 Page(s):64 - 72
Digital Object Identifier 10.1109/4235.843495
Summary: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
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