A grammatical evolution based hyper-heuristic for the automatic design of split criteria

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Marcio Porto Basgalupp and Rodrigo Coelho Barros and 
                 Tiago Barabasz",
  title =        "A grammatical evolution based hyper-heuristic for the
                 automatic design of split criteria",
  booktitle =    "GECCO '14: Proceedings of the 2014 conference on
                 Genetic and evolutionary computation",
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2662-9",
  pages =        "1311--1318",
  keywords =     "genetic algorithms, genetic programming, grammatical
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2576768.2598327",
  DOI =          "doi:10.1145/2576768.2598327",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Top-down induction of decision trees (TDIDT) is a
                 powerful method for data classification. A major issue
                 in TDIDT is the decision on which attribute should be
                 selected for dividing the nodes in subsets, creating
                 the tree. For performing such a task, decision trees
                 make use of a split criterion, which is usually an
                 information-theory based measure. Apparently, there is
                 no free-lunch regarding decision-tree split criteria,
                 as is the case of most things in machine learning. Each
                 application may benefit from a distinct split
                 criterion, and the problem we pose here is how to
                 identify the suitable split criterion for each possible
                 application that may emerge. We propose in this paper a
                 grammatical evolution algorithm for automatically
                 generating split criteria through a context-free
                 grammar. We name our new approach ESC-GE (Evolutionary
                 Split Criteria with Grammatical Evolution). It is
                 empirically evaluated on public gene expression
                 datasets, and we compare its performance with
                 state-of-the-art split criteria, namely the information
                 gain and gain ratio. Results show that ESC-GE
                 outperforms the baseline criteria in the domain of gene
                 expression data, indicating its effectiveness for
                 automatically designing tailor-made split criteria.",
  notes =        "Also known as \cite{2598327} GECCO-2014 A joint
                 meeting of the twenty third international conference on
                 genetic algorithms (ICGA-2014) and the nineteenth
                 annual genetic programming conference (GP-2014)",

Genetic Programming entries for Marcio Porto Basgalupp Rodrigo C Barros Tiago Barabasz