# Learning from imprecise examples with GA-P algorithms.

• Volume: 5, Issue: 2-3, page 305-319
• ISSN: 1134-5632

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## Abstract

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GA-P algorithms combine genetic programming and genetic algorithms to solve symbolic regression problems. In this work, we will learn a model by means of an interval GA-P procedure which can use precise or imprecise examples. This method provides us with an analytic expression that shows the dependence between input and output variables, using interval arithmetic. The method also provides us with interval estimations of the parameters on which this expression depends.The algorithm that we propose has been tested in a practical problem related to electrical engineering. We will obtain an expression of the length of the low voltage electrical line in some Spanish villages as a function of their area and their number of inhabitants. The obtained model is compared to statistical regression-based, neural network, fuzzy rule-based and genetic programming-based models.

## How to cite

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Sánchez, Luciano, and Couso, Inés. "Learning from imprecise examples with GA-P algorithms.." Mathware and Soft Computing 5.2-3 (1998): 305-319. <http://eudml.org/doc/39155>.

@article{Sánchez1998,
abstract = {GA-P algorithms combine genetic programming and genetic algorithms to solve symbolic regression problems. In this work, we will learn a model by means of an interval GA-P procedure which can use precise or imprecise examples. This method provides us with an analytic expression that shows the dependence between input and output variables, using interval arithmetic. The method also provides us with interval estimations of the parameters on which this expression depends.The algorithm that we propose has been tested in a practical problem related to electrical engineering. We will obtain an expression of the length of the low voltage electrical line in some Spanish villages as a function of their area and their number of inhabitants. The obtained model is compared to statistical regression-based, neural network, fuzzy rule-based and genetic programming-based models.},
author = {Sánchez, Luciano, Couso, Inés},
journal = {Mathware and Soft Computing},
keywords = {Algoritmos de aprendizaje; Algoritmos genéticos; Lógica simbólica; Lógica difusa; Regresión; GA-P algorithms; genetic programming; genetic algorithms},
language = {eng},
number = {2-3},
pages = {305-319},
title = {Learning from imprecise examples with GA-P algorithms.},
url = {http://eudml.org/doc/39155},
volume = {5},
year = {1998},
}

TY - JOUR
AU - Sánchez, Luciano
AU - Couso, Inés
TI - Learning from imprecise examples with GA-P algorithms.
JO - Mathware and Soft Computing
PY - 1998
VL - 5
IS - 2-3
SP - 305
EP - 319
AB - GA-P algorithms combine genetic programming and genetic algorithms to solve symbolic regression problems. In this work, we will learn a model by means of an interval GA-P procedure which can use precise or imprecise examples. This method provides us with an analytic expression that shows the dependence between input and output variables, using interval arithmetic. The method also provides us with interval estimations of the parameters on which this expression depends.The algorithm that we propose has been tested in a practical problem related to electrical engineering. We will obtain an expression of the length of the low voltage electrical line in some Spanish villages as a function of their area and their number of inhabitants. The obtained model is compared to statistical regression-based, neural network, fuzzy rule-based and genetic programming-based models.
LA - eng
KW - Algoritmos de aprendizaje; Algoritmos genéticos; Lógica simbólica; Lógica difusa; Regresión; GA-P algorithms; genetic programming; genetic algorithms
UR - http://eudml.org/doc/39155
ER -

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