- author = "Larry M. Deschaine and Frank D. Francone",
- title = "Comparison of Discipulus Linear Genetic Programming Software with Support Vector Machines, Classification Trees, Neural Networks and Human Experts",
- year = "2002",
- type = "White Paper",
- address = "USA",
- keywords = "genetic algorithms, genetic programming, linear genetic programming, SVM, ANN, DT",
-
URL = "
http://www.rmltech.com/doclink/Comparison.White.Paper.pdf",
-
abstract = "Discipulus is multiple-run, linear,
genetic-programming software. Various versions have
been available commercially since 1998 (see,
www.aimlearning.com). Discipulus creates models
directly from data, like neural networks or support
vector machines.
This white paper reports on the result of a multi-year study of the performance of Discipulus by Science Applications International Corp (SAIC) and RML Technologies, Inc. This study compared Discipulus to several other powerful modelling tools on a wide variety of industrial problems including regression and classification problems, CRM problems, time series problems, complex signal discrimination problems and others.
We compared the modeling capability of Discipulus to the following competitive modelling technologies:
Vapnick Statistical Learning,
Neural Networks,
Decision Trees, and
Rule-Based Systems.
In brief summary, the other modelling tools performed inconsistently sometimes they produced very good results and sometimes mediocre or even very poor results. None of these tools produced high quality results across the board. In contrast, Discipulus (at its default settings) always produced results that were the same as or better than the best results from other modelling techniques.
The results described in this white paper have all been previously published in peer-reviewed scientific publications.",
- size = "16 pages",
