Entry Prince:1994:GGT from sigcse1990.bib

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BibTeX entry

@Article{Prince:1994:GGT,
  author =       "Charles Prince and Roger L. Wainwright and Dale A.
                 Schoenefeld and Travis Tull",
  title =        "{GATutor}: a graphical tutorial system for genetic
                 algorithms",
  journal =      j-SIGCSE,
  volume =       "26",
  number =       "1",
  pages =        "203--207",
  month =        mar,
  year =         "1994",
  CODEN =        "SIGSD3",
  DOI =          "https://doi.org/10.1145/191033.191119",
  ISSN =         "0097-8418 (print), 2331-3927 (electronic)",
  ISSN-L =       "0097-8418",
  bibdate =      "Sat Nov 17 18:57:24 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/sigcse1990.bib",
  abstract =     "In this paper we discuss the design and implementation
                 of GATutor, a graphical tutorial system for genetic
                 algorithms (GA). The X Window/Motif system provides
                 powerful tools for the development of a user interfaces
                 with a familiar feel and look. We implemented the
                 Traveling Salesman Problem (TSP) and the Set Covering
                 Problem (SCP) as two example GA problems in the
                 tutorial. The TSP problem uses an order-based
                 chromosome representation (permutation of n objects),
                 while the SCP uses bit strings. The user has numerous
                 buttons to select the GA parameters. These include (a)
                 type of initial population: random or from a file, (b)
                 mode: steady-state or generational, (c) population
                 size, (d) maximum number of generations or trials, (e)
                 generation gap, (f) selection mode, (g) selection bias,
                 (h) selection of the crossover operation from a choice
                 of several possibilities, (i) mutation method, (j)
                 mutation rate, (k) replacement method, (l), elitism,
                 etc. The user has the ability to do a step by step
                 execution or to do a continuous run. The screen layout
                 provides visual representation of the chromosomes in
                 the population with the ability to scroll. This gives
                 the user the option of varying one or two GA parameters
                 to visually see the effect on the algorithm. One of
                 most important features of this tutorial is the set of
                 help screens that explain, with examples, all of the
                 options for each of the GA parameters. This package has
                 already been very useful for teaching the fundamental
                 features of GAs in many different courses, and it has
                 been very valuable in our GA research projects.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIGCSE Bulletin (ACM Special Interest Group on
                 Computer Science Education)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J688",
}

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