Genetically Optimizing the Speed of Programs Evolved to Play Tetris

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InCollection{siegel:1996:aigp2,

• 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 = "http://www1.cs.columbia.edu/~evs/papers/tetris.ps",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277534",
• DOI = "doi:10.7551/mitpress/1109.003.0020",
• 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 technique.",

}

Genetic Programming entries for Eric Siegel Alexander D Chaffee

Citations