Learning from imprecise examples with GA-P algorithms

Laburpena

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.