Genetically Optimizing the Speed of Programs Evolved to Play Tetris
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InCollection{siegel:1996:aigp2,
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author = "Eric V. Siegel and Alexander D. Chaffee",
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title = "Genetically Optimizing the Speed of Programs Evolved
to Play {Tetris}",
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booktitle = "Advances in Genetic Programming 2",
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publisher = "MIT Press",
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year = "1996",
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editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
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pages = "279--298",
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chapter = "14",
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address = "Cambridge, MA, USA",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-262-01158-1",
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URL = "http://www1.cs.columbia.edu/~evs/papers/tetris.ps",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277534",
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DOI = "doi:10.7551/mitpress/1109.003.0020",
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size = "20 pages",
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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
technique.",
- }
Genetic Programming entries for
Eric Siegel
Alexander D Chaffee
Citations