Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Foundations of genetic programming
Foundations of genetic programming
Aspects of adaptation in natural and artificial evolution
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Fuzzy classification systems based on fuzzy information gain measures
Expert Systems with Applications: An International Journal
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A Genetic Programming based approach for solving classification problems is presented in this paper. Classification is understood as the act of placing an object into a set of categories, based on the object's properties; classification algorithms ave designed to learn a function which maps a vector of object features into one of several classes. This is done by analyzing a set of input-output examples ("training samples") of the function. Here we present a method based on the theory of Genetic Algorithms and Genetic Programming that interprets classification problems as optimization problems: Each presented instance of the classification problem is interpreted as an instance of an optimization problem, and a solution is found by a heuristic optimization algorithm. The major new aspects presented in this paper are suitable genetic operators for this problem class (mainly the creation of new hypotheses by merging already existing ones and their detailed evaluation) we have designed and implemented. The experimental part of the paper documents the results produced using new hybrid variants of genetic algorithms as well as investigated parameter settings.