@article {843495,
title = {Interval-valued GA-P algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
volume = {4},
number = {1},
year = {2000},
month = {April},
pages = {64-72},
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.},
keywords = {Arithmetic, Computer science, confidence interval, electrical energy distribution, Fuzzy neural networks, Fuzzy systems, genetic algorithms, Genetic programming, Neural networks, point estimate, Robustness, statistical analysis, symbol manipulation, symbolic regression},
issn = {1089-778X},
doi = {10.1109/4235.843495},
author = {L. S{\'a}nchez}
}