A modular genetic programming system
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Date
2015
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Abstract
Genetic Programming (GP) is an evolutionary algorithm for the automatic
discovery of symbolic expressions, e.g. computer programs or mathematical
formulae, that encode solutions to a user-defined task. Recent advances in GP
systems and computer performance made it possible to successfully apply this
algorithm to real-world applications.
This work offers three main contributions to the state-of-the art in GP
systems:
(I) The documentation of RGP, a state-of-the art GP software implemented as an
extension package to the popular R environment for statistical computation and
graphics. GP and RPG are introduced both formally and with a series of tutorial
examples. As R itself, RGP is available under an open source license.
(II) A comprehensive empirical analysis of modern GP heuristics based on the
methodology of Sequential Parameter Optimization. The effects and interactions
of the most important GP algorithm parameters are analyzed and recommendations
for good parameter settings are given.
(III) Two extensive case studies based on real-world industrial applications.
The first application involves process control models in steel production,
while the second is about meta-model-based optimization of cyclone dust
separators. A comparison with traditional and modern regression methods
reveals that GP offers equal or superior performance in both applications,
with the additional benefit of understandable and easy to deploy models.
Main motivation of this work is the advancement of GP in real-world application
areas. The focus lies on a subset of application areas that are known to be
practical for GP, first of all symbolic regression and classification. It has
been written with practitioners from academia and industry in mind.
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Keywords
Genetic programming, Genetische Programmierung, Symbolic regression, Symbolische Regression, Data mining, Computational intelligence, Big data