Reducing Bloat in GP with Multiple Objectives

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

@InCollection{Bleuler:2008:MPSN,
  author =       "Stefan Bleuler and Johannes Bader and Eckart Zitzler",
  title =        "Reducing Bloat in GP with Multiple Objectives",
  booktitle =    "Multiobjective Problem Solving from Nature: from
                 concepts to applications",
  publisher =    "Springer",
  year =         "2008",
  editor =       "Joshua Knowles and David Corne and Kalyanmoy Deb",
  series =       "Natural Computing",
  chapter =      "9",
  pages =        "177--200",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-72963-1",
  DOI =          "doi:10.1007/978-3-540-72964-8_9",
  abstract =     "This chapter investigates the use of multiobjective
                 techniques in genetic programming (GP) in order to
                 evolve compact programs and to reduce the effects
                 caused by bloating. The underlying approach considers
                 the program size as a second, independent objective
                 besides program functionality, and several studies have
                 found this concept to be successful in reducing bloat.
                 Based on one specific algorithm, we demonstrate the
                 principle of multiobjective GP and show how to apply
                 Pareto-based strategies to GP. This approach
                 outperforms four classical strategies to reduce bloat
                 with regard to both convergence speed and size of the
                 produced programs on an even-parity problem.
                 Additionally, we investigate the question of why the
                 Pareto-based strategies can be more effective in
                 reducing bloat than alternative strategies on several
                 test problems. The analysis falsifies the hypothesis
                 that the small but less functional individuals that are
                 kept in the population act as building blocks building
                 blocks for larger correct solutions. This leads to the
                 conclusion that the advantages are probably due to the
                 increased diversity in the population.",
  notes =        "http://www.springer.com/west/home/computer/artificial?SGWID=4-147-22-173745027-0",
}

Genetic Programming entries for Stefan Bleuler Johannes Bader Eckart Zitzler

Citations