  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 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.
