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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Searson:2015:hbgpa,
  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.",
}

Genetic Programming entries for Dominic Patrick Searson

Citations