%0 Journal Article %1 sanchez:2000:TEC %A Sanchez, Luciano %D 2000 %J IEEE Transactions on Evolutionary Computation %K algorithms, confidence distribution electrical energy estimate, genetic interval, point programming, regression, rural spanish symbolic %N 1 %P 64--72 %T Interval-valued GA-P algorithms %U http://ieeexplore.ieee.org/iel5/4235/18295/00843495.pdf %V 4 %X 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.

@article{sanchez:2000:TEC, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Sanchez, Luciano}, biburl = {https://www.bibsonomy.org/bibtex/2cdb9cb7a43700a56323dcc4282b6b89c/brazovayeye}, interhash = {13bc3b3e408ab8268dd6b037a0c2c136}, intrahash = {cdb9cb7a43700a56323dcc4282b6b89c}, issn = {1089-778X}, journal = {IEEE Transactions on Evolutionary Computation}, keywords = {algorithms, confidence distribution electrical energy estimate, genetic interval, point programming, regression, rural spanish symbolic}, month = {April}, number = 1, pages = {64--72}, size = {9 pages}, timestamp = {2008-06-19T17:51:00.000+0200}, title = {Interval-valued {GA}-{P} algorithms}, url = {http://ieeexplore.ieee.org/iel5/4235/18295/00843495.pdf}, volume = 4, year = 2000 }