A Building Block Approach to Genetic Programming for Rule Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "A. P. Engelbrecht and L. Schoeman and 
                 Sonja Rouwhorst",
  title =        "A Building Block Approach to Genetic Programming for
                 Rule Discovery",
  booktitle =    "Data Mining: A Heuristic Approach",
  publisher =    "IGI-global",
  year =         "2002",
  editor =       "Hussein A. Abbass and Charles S. Newton and 
                 Ruhul Sarker",
  chapter =      "9",
  pages =        "174--190",
  address =      "701 E Chocolate Avenue, Hershey PA 17033, USA",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9781930708259",
  URL =          "http://www.igi-global.com/chapter/building-block-approach-genetic-programming/7589",
  DOI =          "doi:10.4018/978-1-930708-25-9.ch009",
  abstract =     "Genetic programming has recently been used
                 successfully to extract knowledge in the form of
                 IF-THEN rules. For these genetic programming approaches
                 to knowledge extraction from data, individuals
                 represent decision trees. The main objective of the
                 evolutionary process is therefore to evolve the best
                 decision tree, or classifier, to describe the data.
                 Rules are then extracted, after convergence, from the
                 best individual. The current genetic programming
                 approaches to evolve decision trees are computationally
                 complex, since individuals are initialised to complete
                 decision trees.

                 This chapter discusses a new approach to genetic
                 programming for rule extraction, namely the building
                 block approach. This approach starts with individuals
                 consisting of only one building block, and adds new
                 building blocks during the evolutionary process when
                 the simplicity of the individuals cannot account for
                 the complexity in the underlying data. Experimental
                 results are presented and compared with that of C4.5
                 and CN2. The chapter shows that the building block
                 approach achieves very good accuracies compared to that
                 of C4.5 and CN2. It is also shown that the building
                 block approach extracts substantially less rules.",
  notes =        "A. P. Engelbrecht (University of Pretoria, South
                 Africa), L. Schoeman (University of Pretoria, South
                 Africa) and Sonja Rouwhorst (Vrije Universiteit
                 Amsterdam, The Netherlands)",

Genetic Programming entries for Andries P Engelbrecht Lona Schoeman Sonja E Rouwhorst