On Effective and Inexpensive Local Search Techniques in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Fergal Lane and R. Muhammad Atif Azad and Conor Ryan",
  title =        "On Effective and Inexpensive Local Search Techniques
                 in Genetic Programming",
  booktitle =    "13th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2014",
  editor =       "Thomas Bartz-Beielstein and Juergen Branke and 
                 Bogdan Filipic and Jim Smith",
  publisher =    "Springer",
  isbn13 =       "978-3-319-10761-5",
  pages =        "444--453",
  series =       "Lecture Notes in Computer Science",
  address =      "Ljubljana, Slovenia",
  month =        "13-17 " # sep,
  volume =       "8672",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-319-10762-2_44",
  abstract =     "Local search methods can harmoniously work with global
                 search methods such as Evolutionary Algorithms (EAs);
                 however, particularly in Genetic Programming (GP),
                 concerns remain about the additional cost of local
                 search (LS). One successful such system is Chameleon,
                 which tunes internal GP nodes and addresses cost
                 concerns by employing a number of strategies to make
                 its LS both effective and inexpensive. Expense is
                 reduced by an innovative approach to parsimony pressure
                 whereby smaller trees are rewarded with local search
                 opportunities more often than bigger trees.

                 This paper investigates three new extensions to
                 Chameleon's original simple setup, seeking ways for an
                 even more effective local search. These are: trying
                 alternative, more cost-reflective parsimony measures
                 such as visitation length instead of tree size; varying
                 the reward function to more gently encourage parsimony
                 than that in the original setup; and having more tuning
                 earlier in runs when smaller trees can be tuned more
                 cheaply and effectively. These strategies were tested
                 on a varied suite of 16 difficult artificial and
                 real-world regression problems. All of these techniques
                 improved performance.

                 We show that these strategies successfully combined to
                 cumulatively improve average test RMSE by 31percent
                 over the original and already effective Chameleon
                 tuning scheme. A minimum of 64 simulations were run on
                 every problem/tuning setup combination.",
  notes =        "http://ppsn2014.ijs.si/",

Genetic Programming entries for Fergal Lane R Muhammad Atif Azad Conor Ryan