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...
Artificial Vision Systems are commonly used in industrial applications. The low cost of the equipment facilitates the development of new products. In this paper we describe the use of an artificial vision system in one of the phases of a quality control process related to automotive industries: the windshield manufacturing. We intend to localize and classify the defects that were originated while manufacturing the glass that forms the windshield. We will show that a fuzzy classifier, after being...
A new method for applying grammar based Genetic Programming to learn fuzzy rule based classifiers from examples is proposed. It will produce linguistically understandable, rule based definitions in which not all features are sent in the antecedents. A feature selection is implicit in the algorithm. Since both surface and deep structure will be learned, standard grammar based GP is not applicable to this problem. We have adapted GA-P algorithms, a method formerly defined as an hybrid between GA and...
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