- author = "Dominic P. Searson",
- title = "GPTIPS 2: An Open-Source Software Platform for Symbolic Data Mining",
- booktitle = "Handbook of Genetic Programming Applications",
- publisher = "Springer",
- year = "2015",
- editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
- chapter = "22",
- pages = "551--573",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-319-20882-4",
-
URL = "
http://arxiv.org/abs/1412.4690",
-
URL = "
http://arxiv.org/pdf/1412.4690v1.pdf",
-
URL = "http://www.ncl.ac.uk/computing/research/publication/210719",
-
DOI = "
doi:10.1007/978-3-319-20883-1_22",
- size = "25 pages",
-
abstract = "GPTIPS is a free, open source MATLAB based software
platform for symbolic data mining (SDM). It uses a
multigene variant of the biologically inspired machine
learning method of genetic programming (MGGP) as the
engine that drives the automatic model discovery
process. Symbolic data mining is the process of
extracting hidden, meaningful relationships from data
in the form of symbolic equations. In contrast to other
data-mining methods, the structural transparency of the
generated predictive equations can give new insights
into the physical systems or processes that generated
the data. Furthermore, this transparency makes the
models very easy to deploy outside of MATLAB.
The rationale behind GPTIPS is to reduce the technical barriers to using, understanding, visualising and deploying GP based symbolic models of data, whilst at the same time remaining highly customisable and delivering robust numerical performance for power users. In this chapter, notable new features of the latest version of the software (GPTIPS 2) are discussed with these aims in mind. Additionally, a simplified variant of the MGGP high level gene crossover mechanism is proposed.
It is demonstrated that the new functionality of GPTIPS 2 (a) facilitates the discovery of compact symbolic relationships from data using multiple approaches, e.g. using novel gene-centric visualisation analysis to mitigate horizontal bloat and reduce complexity in multigene symbolic regression models (b) provides numerous methods for visualising the properties of symbolic models (c) emphasises the generation of graphically navigable libraries of models that are optimal in terms of the Pareto trade off surface of model performance and complexity and (d) expedites real world applications by the simple, rapid and robust deployment of symbolic models outside the software environment they were developed in.",
