Title | Interval-valued GA-P algorithms |

Publication Type | Journal Article |

Year of Publication | 2000 |

Authors | Sánchez, L. |

Journal | IEEE Transactions on Evolutionary Computation |

Volume | 4 |

Number | 1 |

Pagination | 64-72 |

Date Published | April |

ISSN | 1089-778X |

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 |

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. |

DOI | 10.1109/4235.843495 |