Tweaking a tower of blocks leads to a TMBL: Pursuing long term fitness growth in program evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Tony E. Lewis and George D. Magoulas",
  title =        "Tweaking a tower of blocks leads to a TMBL: Pursuing
                 long term fitness growth in program evolution",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  pages =        "4465--4472",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, TMBL,
                 evolutionary computation, genetic programming, long
                 term fitness growth, program evolution, tower of
                 blocks, tweaking mutation behaviour learning,
                 behavioural sciences computing, biology computing",
  isbn13 =       "978-1-4244-6910-9",
  DOI =          "doi:10.1109/CEC.2010.5586375",
  abstract =     "If a population of programs evolved not for a few
                 hundred generations but for a few hundred thousand or
                 more, could it generate more interesting behaviours and
                 tackle more complex problems? We begin to investigate
                 this question by introducing Tweaking Mutation
                 Behaviour Learning (TMBL), a form of evolutionary
                 computation designed to meet this challenge. Whereas
                 Genetic Programming (GP) typically involves creating a
                 large pool of initial solutions and then shuffling them
                 (with crossover and mutation) over relatively few
                 generations, TMBL focuses on the cumulative acquisition
                 of small adaptive mutations over many generations. In
                 particular, we aim to reduce limits on long term
                 fitness growth by encouraging tweaks: changes which
                 affect behaviour without ruining the existing
                 functionality. We use this notion to construct a
                 standard representation for TMBL. We then
                 experimentally compare TMBL against linear GP and
                 tree-based GP and find that TMBL shows strong signs of
                 being more conducive to the long term growth of
  DOI =          "doi:10.1109/CEC.2010.5586375",
  notes =        "WCCI 2010. Also known as \cite{5586375}",

Genetic Programming entries for Tony Lewis George D Magoulas