Short term memory in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "K. Bearpark and A. J. Keane",
  title =        "Short term memory in genetic programming",
  booktitle =    "Fourth International Conference on Adaptive Computing
                 in Design and Manufacture, ACDM '00",
  year =         "2000",
  editor =       "I. C. Parmee",
  pages =        "309--320",
  address =      "University of Plymouth, Devon, UK",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
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  size =         "12 pages",
  abstract =     "The recognition of useful information, its retention
                 in memory, and subsequent use plays an important part
                 in the behaviour of many biological species.
                 Information gained by experience in one generation can
                 be propagated to subsequent generations by some form of
                 teaching. Each generation can then supplement its
                 taught learning by its own experience. In this paper we
                 explore the role of memorised information in the
                 performance of a Genetic Programming (GP) system that
                 uses a tree structure as its representation. Memory is
                 implemented in the form of a set of subtrees derived
                 from successful members of each generation. The memory
                 is used by a genetic operator similar to the mutation
                 operator but with the following difference. In a
                 tree-structured system the mutation operator replaces
                 randomly selected sub-trees by new randomly-generated
                 sub-trees. The memory operator replaces randomly
                 selected sub-trees by sub-trees randomly randomly
                 selected from the memory. To study the memory
                 operator's impact a GP system is used to evolve a
                 well-known expression from classical kinetics using
                 fitness-based selection. The memory operator is used
                 together with the common crossover and mutation
                 operators. It is shown that the addition of a memory
                 operator increases the probability of a successful
                 evolution for this particular problem. At this stage we
                 make no claim for its impact on other problems that
                 have been successfully addressed by Genetic
  notes =        "Evolutionary Design and Manufacture: Selected Papers
                 from . (ACDM '00) One example physics integration of
                 u*t+0.5*a*t*t t=1...10, u=20 or u=200 a=980 Reverse
                 Polish RPN except for first (in Lisp) max length=11??
                 19??, roulette wheel, crossover, mutation. Memory
                 operator: when fitness improves over best of previous
                 generation whole of tree and its subtrees are saved in
                 memory. Later random choices from memory.
                 elitism.Pop=2000, gen=20 40000 tests per minute (300

Genetic Programming entries for Keith Bearpark Andy J Keane