Kaizen programming

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

@InProceedings{DeMelo:2014:GECCO,
  author =       "Vinicius Veloso {De Melo}",
  title =        "Kaizen programming",
  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 =        "895--902",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2576768.2598264",
  DOI =          "doi:10.1145/2576768.2598264",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper presents Kaizen Programming, an
                 evolutionary tool based on the concepts of Continuous
                 Improvement from Kaizen Japanese methodology. One may
                 see Kaizen Programming as a new paradigm since, as
                 opposed to classical evolutionary algorithms where
                 individuals are complete solutions, in Kaizen
                 Programming each expert proposes an idea to solve part
                 of the problem, thus a solution is composed of all
                 ideas together. Consequently, evolution becomes a
                 collaborative approach instead of an egocentric one. An
                 idea's quality (analog to an individual's fitness) is
                 not how good it fits the data, but a measurement of its
                 contribution to the solution, which improves the
                 knowledge about the problem. Differently from
                 evolutionary algorithms that simply perform
                 trial-and-error search, one can determine, exactly,
                 parts of the solution that should be removed or
                 improved. That property results in the reduction in
                 bloat, number of function evaluations, and computing
                 time. Even more important, the Kaizen Programming tool,
                 proposed to solve symbolic regression problems, builds
                 the solutions as linear regression models - not linear
                 in the variables, but linear in the parameters, thus
                 all properties and characteristics of such statistical
                 tool are valid. Experiments on benchmark functions
                 proposed in the literature show that Kaizen Programming
                 easily outperforms Genetic Programming and other
                 methods, providing high quality solutions for both
                 training and testing sets while requiring a small
                 number of function evaluations.",
  notes =        "Also known as \cite{2598264} 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 Vinicius Veloso de Melo

Citations