Created by W.Langdon from gp-bibliography.bib Revision:1.7954

- @Article{sanchez:2000:TEC,
- author = "Luciano Sanchez",
- title = "Interval-valued GA-P algorithms",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2000",
- volume = "4",
- number = "1",
- pages = "64--72",
- month = apr,
- keywords = "genetic algorithms, genetic programming, symbolic regression, point estimate, confidence interval, rural spanish electrical energy distribution",
- ISSN = "1089-778X",
- URL = "http://ieeexplore.ieee.org/iel5/4235/18295/00843495.pdf",
- size = "9 pages",
- 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.",
- }

Genetic Programming entries for Luciano Sanchez