GPTIPS 2: An Open-Source Software Platform for Symbolic Data Mining

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.

Hybrid Single Node Genetic Programming for Symbolic Regression

“…Recently, several methods emerged [1], [2], [15], [21], [22] that explicitly restrict the class of models to generalized linear models, i.e. to a linear combination of possibly non-linear basis functions.…”

“…GPTIPS [21], [22] is an open-source SR toolbox for MATLAB. It is an implementation of Multi-Gene Genetic Programming (MGGP) [8] and thus has its roots in classical GP.…”

Genetic programming for experimental big data mining: A case study on concrete creep formulation

“…In contrast to standard symbolic regression, MGGP allows the evolution of accurate and relatively compact mathematical models. Even when large numbers of input variables are used, this technique can automatically select the most contributed variables in the model, formulate the structure of the model, and solve the coefficients in the regression equation [16][17][18][19]. Therefore, unlike other techniques such as traditional regression analysis or ANN, there is no need in the MGGP technique for the user to pre-define the formulation structure of the model or select any existing form of the relationship for optimization [3,4], which makes it more practical for complex optimization problems.…”

Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm

“…The parameters are randomly generated from the predefined bounded interval [-10, 10] [36]. In this stage, node functions, size of population, maximum depth of tree and maximum number of genes need to be set to control the generation.…”

“…SVR is executed in R with the package 'e1071'. The code for the presented model SR is written in Matlab using the GPTIPS 2 tool box, which is available online [36]. These two models are used Maximum amount of time to run=180 s d=1 C=8…”