Pyramid search: Finding solutions for deceptive problems quickly in genetic programming

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@InProceedings{ciesielski:2003:psfsfdpqigp,
  author =       "Vic Ciesielski and Xiang Li",
  title =        "Pyramid search: Finding solutions for deceptive
                 problems quickly in genetic programming",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "936--943",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming, Australia,
                 Computer science, Information technology, Parallel
                 processing, probability, search problems, deceptive
                 problem, discard process, evolve process, probability,
                 pyramid search strategy, standard deviation",
  ISBN =         "0-7803-7804-0",
  DOI =          "doi:10.1109/CEC.2003.1299767",
  abstract =     "In deceptive problems many runs lead to suboptimal
                 solutions and it can be difficult to escape from these
                 local optima and find the global best solution. We
                 propose a pyramid search strategy for these kinds of
                 problems. In the pyramid strategy a number of
                 populations are initialised and independently evolved
                 for a number of generations at which point the worst
                 performing populations are discarded. This
                 evolve/discard process is continued until the problem
                 is solved or one population remains. We show that for a
                 number of deceptive problems the pyramid strategy
                 results in a higher probability of success with fewer
                 evaluations and a lower standard deviation of the
                 number evaluations to success than the conventional
                 approach of running to a maximum number of generations
                 and then restarting.",
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",
}

Genetic Programming entries for Victor Ciesielski Xiang Li

Citations