Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Learning in the presence of concept drift and hidden contexts
Machine Learning
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Foundations of genetic programming
Foundations of genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Online modelling based on Genetic Programming
International Journal of Intelligent Systems Technologies and Applications
Genetic Programming and Evolvable Machines
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Virtual sensors are a key element in many modern control and diagnosis systems, and their importance is continuously increasing; if there are no appropriate models available, virtual sensor design has to be based on data. Structure identification using Genetic Programming is a method whose ability to produce models of high quality has been shown in many theoretical contributions as well as empirical test reports. One of its most prominent shortcomings is relatively high runtime consumption; additionally, one often has to deal with problems such as overfitting and the selection of optimal models out of a pool of potential models that are able to reproduce the given training data. In this article we present a sliding window approach that is applicable for Genetic Programming based structure identification; the selection pressure, a value measuring how hard it is to produce better models on the basis of the current population, is used for triggering the sliding window behavior. Furthermore, we demonstrate how this mechanism is able to reduce runtime consumption as well as to help finding even better models with respect to test data not considered by the training algorithm.