Prevention of Early Convergence in Genetic Programming by Replacement of Similar Programs

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

  author =       "Vic Ciesielski and Dylan Mawhinney",
  title =        "Prevention of Early Convergence in Genetic Programming
                 by Replacement of Similar Programs",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and 
                 Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and 
                 Mark Shackleton",
  pages =        "67--72",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  month =        "12-17 " # may,
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)

                 See also \cite{oai:CiteSeerPSU:451316}",
  keywords =     "genetic algorithms, genetic programming, CPU time, MAX
                 problem, early convergence prevention, experimental
                 work, fitness function, mutation, mutation rate,
                 premature convergence, randomly generated programs,
                 similar program replacement, similarity matching,
                 soccer playing programs, convergence, programming",
  DOI =          "doi:10.1109/CEC.2002.1006211",
  abstract =     "We have investigated an approach to preventing or
                 minimising the occurrence of premature convergence by
                 measuring the similarity between the programs in the
                 population and replacing the most similar ones with
                 randomly generated programs. On a problem with known
                 premature convergence behaviour, the MAX problem,
                 similarity replacement significantly decreased the rate
                 of premature convergence over the best that could be
                 achieved by manipulation of the mutation rate. The
                 expected CPU time for a successful run was increased
                 due to the additional cost of the similarity matching.
                 On a problem which has a very expensive fitness
                 function, the evolution of a team of soccer playing
                 programs, the degree of premature convergence rate was
                 also significantly reduced. However, in this case the
                 expected time for a successful run was significantly
                 decreased indicating that similarity replacement can be
                 worthwhile for problems with expensive evaluation
                 functions. A significant discovery from our
                 experimental work is that a small change to the way
                 mutation is carried out can result in significant
                 reductions in premature convergence",

Genetic Programming entries for Victor Ciesielski Dylan Mawhinney