- author = "Oliver Flasch",
- title = "A modular genetic programming system",
- school = "Fakultaet fuer Informatik, Technische Universitaet Dortmund",
- year = "2015",
- address = "Germany",
- month = "6 " # may,
- keywords = "genetic algorithms, genetic programming, genetische programmierung, symbolic regression, symbolische regression, data mining, computational intelligence, big data",
- bibsource = "OAI-PMH server at eldorado.uni-dortmund.de",
- contributor = "Guenter Rudolph and",
- language = "eng",
- oai = "oai:eldorado.tu-dortmund.de:2003/34162",
-
URL = "
https://eldorado.tu-dortmund.de/bitstream/2003/34162/1/Dissertation.pdf",
-
URL = "
http://hdl.handle.net/2003/34162",
-
DOI = "
10.17877/DE290R-7807",
- size = "208 pages",
- 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 Optimisation. The effects and interactions of the most important GP algorithm parameters are analysed 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 optimisation 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.",
-
notes = "Supervisor Thomas Bartz-Beielstein LS 11. RGP. Meta
Models for Cyclone Dust Separators (AppDust). Roll
Train Control Models (AppSteel)
Figure 3.1: Features versus costs of modern GP system offerings: RGP ECJ DataModeler Eureqa DataModeler tinyGP",
