db_connect: Could not connect to paper db at "wotug@dragon.kent.ac.uk"<br>
db_connect: Could not connect to paper db at "wotug@dragon.kent.ac.uk"<br>
@InProceedings{Moore03,
  title =        "{A}ccurate {C}alculation of {D}eme {S}izes for a {P}arallel {G}enetic {S}cheduling {A}lgorithm",
db_connect: Could not connect to paper db at "wotug@dragon.kent.ac.uk"<br>
  author=        "Moore,  M.",
db_connect: Could not connect to paper db at "wotug@dragon.kent.ac.uk"<br>
  editor=        "Broenink,  Jan F. and Hilderink,  Gerald H.",
db_connect: Could not connect to paper db at "wotug@dragon.kent.ac.uk"<br>
  pages =        "305--313",
  booktitle=     "{C}ommunicating {P}rocess {A}rchitectures 2003",
  isbn=          "1 58603 381 6",
  year=          "2003",
  month=         "sep",
  abstract=      "The accuracies of three equations to determine the size of
     populations for serial and parallel genetic algorithms are
     evaluated when applied to a parallel genetic algorithm that
     schedules tasks on a cluster of computers connected via
     shared bus. This NP-complete problem is representative of a
     variety of optimisation problems for which genetic
     algorithms (GAs) have been shown to effectively approximate
     the optimal solution. However, empirical determination of
     parameters needed by both serial and parallel GAs is
     time-consuming, often impractically so in production
     environments. The ability to predetermine parameter values
     mathematically eliminates this difficulty. The parameter
     that exerts the most influence over the solution quality of
     a parallel genetic algorithm is the population size of the
     demes. Comparisons here show that the most accurate equation
     for the scheduling application is Cant\`{u}-Paz' serial
     population sizing calculation based on the gambler's ruin
     model [1]. The study presented below is part of an ongoing
     analysis of the effectiveness of parallel genetic algorithm
     parameter value computations based on schema theory. The
     study demonstrates that the correct deme size can be
     predetermined quantitatively for the scheduling problem
     presented here, and suggests that this may also be true for
     similar optimisation problems. This work is supported by
     NASA Grant NAG9-140."
}