Paper Details

@InProceedings{Moore01,
  title = "{P}arallel {G}enetic {A}lgorithms to {F}ind {N}ear {O}ptimal {S}chedules for {T}asks on {M}ultiprocessor {A}rchitectures",
  author= "Moore, M.",
  editor= "Chalmers, Alan G. and Mirmehdi, Majid and Muller, Henk",
  pages = "27--36",
  booktitle= "{C}ommunicating {P}rocess {A}rchitectures 2001",
  isbn= "1 58603 202 X",
  year= "2001",
  month= "sep",
  abstract= "Parallel genetic schedulers (PGS) are applied to a
     combinatorial optimisation problem, the scheduling of
     multiple, independent, non-identical tasks. The tasks are
     functionally partitioned and must be distributed over a
     multicomputer or multiprocessor system. As each task
     completes execution, a result message must be communicated.
     Communication occurs over a shared bus. This problem is
     known to be NP-complete [1]. The PGS execute on a shared
     memory multiprocessor system and on a simulated SIMD torus.
     Schedules produced by the PGS are compared to each other, to
     those found by an exponential-time optimal branch and bound
     algorithm, and to those found by a linear-time opportunistic
     algorithm. The PGS produce extremely accurate schedules very
     quickly. When the PGS are executed with increasing numbers
     of processors, near linear speedups are obtained with no
     decrease in the quality of the resulting schedules."
}

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