Genetically Optimizing the Speed of Programs Evolved to Play Tetris

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Eric V. Siegel and Alexander D. Chaffee",
  title =        "Genetically Optimizing the Speed of Programs Evolved
                 to Play Tetris",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "279--298",
  chapter =      "14",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  URL =          "",
  URL =          "",
  size =         "20 pages",
  abstract =     "Many new domains for genetic programming require
                 evolved programs to be executed for longer amounts of
                 time. For these applications it is likely that some
                 test cases optimally require more computation cycles
                 than others. Therefore, programs must dynamically
                 allocate cycles among test cases in order to use
                 computation time efficiently. To elicit the strategic
                 allocation of computation time, we impose an aggregate
                 computation time ceiling that applies over a series of
                 fitness cases. This exerts time pressure on evolved
                 programs, with the effect that resulting programs
                 dynamically allocate computation time,
                 opportunistically spending less time per test case when
                 possible, with minimal damage to domain performance.
                 This technique is in principle extensible to resources
                 other than computation time such as memory or fuel. We
                 introduce the game Tetris as a test problem for this

Genetic Programming entries for Eric Siegel Alexander D Chaffee